
> < 
04.06.2021, 00:20  #19501 (permalink)  

Links Tor sites deep http://darknetlinks.net
Exclusive to the aggio kiev ua <a href=toronionurlsdir biz>List of links to onion sites dark Internet</a> or quick access to the resources of the cover Internet, contemn the directory onion sites It contains all known pages that are available at worst in the TOR network For thoughtless access to the resources of the safety Internet, do the directory onion sites It contains all known pages that are within reach how on earth in the TOR network mobile Recondite Wiki Tor darkweb2020 com
__________________
http://torlinks.site 

04.06.2021, 01:45  #19505 (permalink)  

Statistical association football predictions
cutt ly/tgUsZ9U i ibb co/rbgFnyV/fixedmatches jpg
bit ly/3dWKdMz i ibb co/sJYkHnZ/join gif Predicting Football Results With Statistical Modelling Combining the world’s most popular sport with everyone’s favourite discrete probability distribution, this post predicts football matches using the Poisson distribution David Sheehan Data scientist interested in sports, politics and Simpsons references Football (or soccer to my American readers) is full of clichés: “It’s a game of two halves”, “taking it one game at a time” and “Liverpool have failed to win the Premier League” You’re less likely to hear “Treating the number of goals scored by each team as independent Poisson processes, statistical modelling suggests that the home team have a 60% chance of winning today” But this is actually a bit of cliché too (it has been discussed here, here, here, here and particularly well here) As we’ll discover, a simple Poisson model is, well, overly simplistic But it’s a good starting point and a nice intuitive way to learn about statistical modelling So, if you came here looking to make money, I hear this guy makes £5000 per month without leaving the house Poisson Distribution HomeTeam AwayTeam HomeGoals AwayGoals 0 Burnley Swansea 0 1 1 Crystal Palace West Brom 0 1 2 Everton Tottenham 1 1 3 Hull Leicester 2 1 4 Man City Sunderland 2 1 We imported a csv as a pandas dataframe, which contains various information for each of the 380 EPL games in the 201617 English Premier League season We restricted the dataframe to the columns in which we’re interested (specifically, team names and numer of goals scored by each team) I’ll omit most of the code that produces the graphs in this post But don’t worry, you can find that code on my github page Our task is to model the final round of fixtures in the season, so we must remove the last 10 rows (each gameweek consists of 10 matches) You’ll notice that, on average, the home team scores more goals than the away team This is the so called ‘home (field) advantage’ (discussed here) and isn’t specific to soccer This is a convenient time to introduce the Poisson distribution It’s a discrete probability distribution that describes the probability of the number of events within a specific time period (e g 90 mins) with a known average rate of occurrence A key assumption is that the number of events is independent of time In our context, this means that goals don’t become more/less likely by the number of goals already scored in the match Instead, the number of goals is expressed purely as function an average rate of goals If that was unclear, maybe this mathematical formulation will make clearer: represents the average rate (e g average number of goals, average number of letters you receive, etc ) So, we can treat the number of goals scored by the home and away team as two independent Poisson distributions The plot below shows the proportion of goals scored compared to the number of goals estimated by the corresponding Poisson distributions We can use this statistical model to estimate the probability of specfic events The probability of a draw is simply the sum of the events where the two teams score the same amount of goals Note that we consider the number of goals scored by each team to be independent events (i e P(A n B) = P(A) P(B)) The difference of two Poisson distribution is actually called a Skellam distribution So we can calculate the probability of a draw by inputting the mean goal values into this distribution So, hopefully you can see how we can adapt this approach to model specific matches We just need to know the average number of goals scored by each team and feed this data into a Poisson model Let’s have a look at the distribution of goals scored by Chelsea and Sunderland (teams who finished 1st and last, respectively) Building A Model You should now be convinced that the number of goals scored by each team can be approximated by a Poisson distribution Due to a relatively sample size (each team plays at most 19 home/away games), the accuracy of this approximation can vary significantly (especially earlier in the season when teams have played fewer games) Similar to before, we could now calculate the probability of various events in this Chelsea Sunderland match But rather than treat each match separately, we’ll build a more general Poisson regression model (what is that?) Generalized Linear Model Regression Results Dep Variable: goals No Observations: 740 Model: GLM Df Residuals: 700 Model Family: Poisson Df Model: 39 Link Function: log Scale: 1 0 Method: IRLS LogLikelihood: 1042 4 Date: Sat, 10 Jun 2017 Deviance: 776 11 Time: 11:17:38 Pearson chi2: 659 No Iterations: 8 coef std err z P>z [95 0% Conf Int ] Intercept 0 3725 0 198 1 880 0 060 0 016 0 761 team[T Bournemouth] 0 2891 0 179 1 612 0 107 0 641 0 062 team[T Burnley] 0 6458 0 200 3 230 0 001 1 038 0 254 team[T Chelsea] 0 0789 0 162 0 488 0 626 0 238 0 396 team[T Crystal Palace] 0 3865 0 183 2 107 0 035 0 746 0 027 team[T Everton] 0 2008 0 173 1 161 0 246 0 540 0 138 team[T Hull] 0 7006 0 204 3 441 0 001 1 100 0 302 team[T Leicester] 0 4204 0 187 2 249 0 025 0 787 0 054 team[T Liverpool] 0 0162 0 164 0 099 0 921 0 306 0 338 team[T Man City] 0 0117 0 164 0 072 0 943 0 310 0 334 team[T Man United] 0 3572 0 181 1 971 0 049 0 713 0 002 team[T Middlesbrough] 1 0087 0 225 4 481 0 000 1 450 0 568 team[T Southampton] 0 5804 0 195 2 976 0 003 0 963 0 198 team[T Stoke] 0 6082 0 197 3 094 0 002 0 994 0 223 team[T Sunderland] 0 9619 0 222 4 329 0 000 1 397 0 526 team[T Swansea] 0 5136 0 192 2 673 0 008 0 890 0 137 team[T Tottenham] 0 0532 0 162 0 328 0 743 0 265 0 371 team[T Watford] 0 5969 0 197 3 035 0 002 0 982 0 211 team[T West Brom] 0 5567 0 194 2 876 0 004 0 936 0 177 team[T West Ham] 0 4802 0 189 2 535 0 011 0 851 0 109 opponent[T Bournemouth] 0 4109 0 196 2 092 0 036 0 026 0 796 opponent[T Burnley] 0 1657 0 206 0 806 0 420 0 237 0 569 opponent[T Chelsea] 0 3036 0 234 1 298 0 194 0 762 0 155 opponent[T Crystal Palace] 0 3287 0 200 1 647 0 100 0 062 0 720 opponent[T Everton] 0 0442 0 218 0 202 0 840 0 472 0 384 opponent[T Hull] 0 4979 0 193 2 585 0 010 0 120 0 875 opponent[T Leicester] 0 3369 0 199 1 694 0 090 0 053 0 727 opponent[T Liverpool] 0 0374 0 217 0 172 0 863 0 463 0 389 opponent[T Man City] 0 0993 0 222 0 448 0 654 0 534 0 335 opponent[T Man United] 0 4220 0 241 1 754 0 079 0 894 0 050 opponent[T Middlesbrough] 0 1196 0 208 0 574 0 566 0 289 0 528 opponent[T Southampton] 0 0458 0 211 0 217 0 828 0 369 0 460 opponent[T Stoke] 0 2266 0 203 1 115 0 265 0 172 0 625 opponent[T Sunderland] 0 3707 0 198 1 876 0 061 0 017 0 758 opponent[T Swansea] 0 4336 0 195 2 227 0 026 0 052 0 815 opponent[T Tottenham] 0 5431 0 252 2 156 0 031 1 037 0 049 opponent[T Watford] 0 3533 0 198 1 782 0 075 0 035 0 742 opponent[T West Brom] 0 0970 0 209 0 463 0 643 0 313 0 507 opponent[T West Ham] 0 3485 0 198 1 758 0 079 0 040 0 737 home 0 2969 0 063 4 702 0 000 0 173 0 421 If you’re curious about the smf glm( ) part, you can find more information here (edit: earlier versions of this post had erroneously employed a Generalised Estimating Equation (GEE) what’s the difference?) I’m more interested in the values presented in the coef column in the model summary table, which are analogous to the slopes in linear regression Similar to logistic regression, we take the exponent of the parameter values A positive value implies more goals (), while values closer to zero represent more neutral effects () Towards the bottom of the table you might notice that home has a coef of 0 2969 This captures the fact that home teams generally score more goals than the away team (specifically, =1 35 times more likely) But not all teams are created equal Chelsea has a coef of 0 0789, while the corresponding value for Sunderland is 0 9619 (sort of saying Chelsea (Sunderland) are better (much worse!) scorers than average) Finally, the opponent* values penalize/reward teams based on the quality of the opposition This relfects the defensive strength of each team (Chelsea: 0 3036; Sunderland: 0 3707) In other words, you’re less likely to score against Chelsea Hopefully, that all makes both statistical and intuitive sense Let’s start making some predictions for the upcoming matches We simply pass our teams into poisson_model and it’ll return the expected average number of goals for that team (we need to run it twice we calculate the expected average number of goals for each team separately) So let’s see how many goals we expect Chelsea and Sunderland to score Predicting Football Results With Statistical Modelling Combining the world’s most popular sport with everyone’s favourite discrete probability distribution, this post predicts football matches using the Poisson distribution David Sheehan Data scientist interested in sports, politics and Simpsons references Football (or soccer to my American readers) is full of clichés: “It’s a game of two halves”, “taking it one game at a time” and “Liverpool have failed to win the Premier League” You’re less likely to hear “Treating the number of goals scored by each team as independent Poisson processes, statistical modelling suggests that the home team have a 60% chance of winning today” But this is actually a bit of cliché too (it has been discussed here, here, here, here and particularly well here) As we’ll discover, a simple Poisson model is, well, overly simplistic But it’s a good starting point and a nice intuitive way to learn about statistical modelling So, if you came here looking to make money, I hear this guy makes £5000 per month without leaving the house Poisson Distribution HomeTeam AwayTeam HomeGoals AwayGoals 0 Burnley Swansea 0 1 1 Crystal Palace West Brom 0 1 2 Everton Tottenham 1 1 3 Hull Leicester 2 1 4 Man City Sunderland 2 1 We imported a csv as a pandas dataframe, which contains various information for each of the 380 EPL games in the 201617 English Premier League season We restricted the dataframe to the columns in which we’re interested (specifically, team names and numer of goals scored by each team) I’ll omit most of the code that produces the graphs in this post But don’t worry, you can find that code on my github page Our task is to model the final round of fixtures in the season, so we must remove the last 10 rows (each gameweek consists of 10 matches) You’ll notice that, on average, the home team scores more goals than the away team This is the so called ‘home (field) advantage’ (discussed here) and isn’t specific to soccer This is a convenient time to introduce the Poisson distribution It’s a discrete probability distribution that describes the probability of the number of events within a specific time period (e g 90 mins) with a known average rate of occurrence A key assumption is that the number of events is independent of time In our context, this means that goals don’t become more/less likely by the number of goals already scored in the match Instead, the number of goals is expressed purely as function an average rate of goals If that was unclear, maybe this mathematical formulation will make clearer: represents the average rate (e g average number of goals, average number of letters you receive, etc ) So, we can treat the number of goals scored by the home and away team as two independent Poisson distributions The plot below shows the proportion of goals scored compared to the number of goals estimated by the corresponding Poisson distributions We can use this statistical model to estimate the probability of specfic events The probability of a draw is simply the sum of the events where the two teams score the same amount of goals Note that we consider the number of goals scored by each team to be independent events (i e P(A n B) = P(A) P(B)) The difference of two Poisson distribution is actually called a Skellam distribution So we can calculate the probability of a draw by inputting the mean goal values into this distribution So, hopefully you can see how we can adapt this approach to model specific matches We just need to know the average number of goals scored by each team and feed this data into a Poisson model Let’s have a look at the distribution of goals scored by Chelsea and Sunderland (teams who finished 1st and last, respectively) Building A Model You should now be convinced that the number of goals scored by each team can be approximated by a Poisson distribution Due to a relatively sample size (each team plays at most 19 home/away games), the accuracy of this approximation can vary significantly (especially earlier in the season when teams have played fewer games) Similar to before, we could now calculate the probability of various events in this Chelsea Sunderland match But rather than treat each match separately, we’ll build a more general Poisson regression model (what is that?) Generalized Linear Model Regression Results Dep Variable: goals No Observations: 740 Model: GLM Df Residuals: 700 Model Family: Poisson Df Model: 39 Link Function: log Scale: 1 0 Method: IRLS LogLikelihood: 1042 4 Date: Sat, 10 Jun 2017 Deviance: 776 11 Time: 11:17:38 Pearson chi2: 659 No Iterations: 8 coef std err z P>z [95 0% Conf Int ] Intercept 0 3725 0 198 1 880 0 060 0 016 0 761 team[T Bournemouth] 0 2891 0 179 1 612 0 107 0 641 0 062 team[T Burnley] 0 6458 0 200 3 230 0 001 1 038 0 254 team[T Chelsea] 0 0789 0 162 0 488 0 626 0 238 0 396 team[T Crystal Palace] 0 3865 0 183 2 107 0 035 0 746 0 027 team[T Everton] 0 2008 0 173 1 161 0 246 0 540 0 138 team[T Hull] 0 7006 0 204 3 441 0 001 1 100 0 302 team[T Leicester] 0 4204 0 187 2 249 0 025 0 787 0 054 team[T Liverpool] 0 0162 0 164 0 099 0 921 0 306 0 338 team[T Man City] 0 0117 0 164 0 072 0 943 0 310 0 334 team[T Man United] 0 3572 0 181 1 971 0 049 0 713 0 002 team[T Middlesbrough] 1 0087 0 225 4 481 0 000 1 450 0 568 team[T Southampton] 0 5804 0 195 2 976 0 003 0 963 0 198 team[T Stoke] 0 6082 0 197 3 094 0 002 0 994 0 223 team[T Sunderland] 0 9619 0 222 4 329 0 000 1 397 0 526 team[T Swansea] 0 5136 0 192 2 673 0 008 0 890 0 137 team[T Tottenham] 0 0532 0 162 0 328 0 743 0 265 0 371 team[T Watford] 0 5969 0 197 3 035 0 002 0 982 0 211 team[T West Brom] 0 5567 0 194 2 876 0 004 0 936 0 177 team[T West Ham] 0 4802 0 189 2 535 0 011 0 851 0 109 opponent[T Bournemouth] 0 4109 0 196 2 092 0 036 0 026 0 796 opponent[T Burnley] 0 1657 0 206 0 806 0 420 0 237 0 569 opponent[T Chelsea] 0 3036 0 234 1 298 0 194 0 762 0 155 opponent[T Crystal Palace] 0 3287 0 200 1 647 0 100 0 062 0 720 opponent[T Everton] 0 0442 0 218 0 202 0 840 0 472 0 384 opponent[T Hull] 0 4979 0 193 2 585 0 010 0 120 0 875 opponent[T Leicester] 0 3369 0 199 1 694 0 090 0 053 0 727 opponent[T Liverpool] 0 0374 0 217 0 172 0 863 0 463 0 389 opponent[T Man City] 0 0993 0 222 0 448 0 654 0 534 0 335 opponent[T Man United] 0 4220 0 241 1 754 0 079 0 894 0 050 opponent[T Middlesbrough] 0 1196 0 208 0 574 0 566 0 289 0 528 opponent[T Southampton] 0 0458 0 211 0 217 0 828 0 369 0 460 opponent[T Stoke] 0 2266 0 203 1 115 0 265 0 172 0 625 opponent[T Sunderland] 0 3707 0 198 1 876 0 061 0 017 0 758 opponent[T Swansea] 0 4336 0 195 2 227 0 026 0 052 0 815 opponent[T Tottenham] 0 5431 0 252 2 156 0 031 1 037 0 049 opponent[T Watford] 0 3533 0 198 1 782 0 075 0 035 0 742 opponent[T West Brom] 0 0970 0 209 0 463 0 643 0 313 0 507 opponent[T West Ham] 0 3485 0 198 1 758 0 079 0 040 0 737 home 0 2969 0 063 4 702 0 000 0 173 0 421 If you’re curious about the smf glm( ) part, you can find more information here (edit: earlier versions of this post had erroneously employed a Generalised Estimating Equation (GEE) what’s the difference?) I’m more interested in the values presented in the coef column in the model summary table, which are analogous to the slopes in linear regression Similar to logistic regression, we take the exponent of the parameter values A positive value implies more goals (), while values closer to zero represent more neutral effects () Towards the bottom of the table you might notice that home has a coef of 0 2969 This captures the fact that home teams generally score more goals than the away team (specifically, =1 35 times more likely) But not all teams are created equal Chelsea has a coef of 0 0789, while the corresponding value for Sunderland is 0 9619 (sort of saying Chelsea (Sunderland) are better (much worse!) scorers than average) Finally, the opponent* values penalize/reward teams based on the quality of the opposition This relfects the defensive strength of each team (Chelsea: 0 3036; Sunderland: 0 3707) In other words, you’re less likely to score against Chelsea Hopefully, that all makes both statistical and intuitive sense Let’s start making some predictions for the upcoming matches We simply pass our teams into poisson_model and it’ll return the expected average number of goals for that team (we need to run it twice we calculate the expected average number of goals for each team separately) So let’s see how many goals we expect Chelsea and Sunderland to score Predicting Football Results With Statistical Modelling Combining the world’s most popular sport with everyone’s favourite discrete probability distribution, this post predicts football matches using the Poisson distribution David Sheehan Data scientist interested in sports, politics and Simpsons references Football (or soccer to my American readers) is full of clichés: “It’s a game of two halves”, “taking it one game at a time” and “Liverpool have failed to win the Premier League” You’re less likely to hear “Treating the number of goals scored by each team as independent Poisson processes, statistical modelling suggests that the home team have a 60% chance of winning today” But this is actually a bit of cliché too (it has been discussed here, here, here, here and particularly well here) As we’ll discover, a simple Poisson model is, well, overly simplistic But it’s a good starting point and a nice intuitive way to learn about statistical modelling So, if you came here looking to make money, I hear this guy makes £5000 per month without leaving the house Poisson Distribution HomeTeam AwayTeam HomeGoals AwayGoals 0 Burnley Swansea 0 1 1 Crystal Palace West Brom 0 1 2 Everton Tottenham 1 1 3 Hull Leicester 2 1 4 Man City Sunderland 2 1 We imported a csv as a pandas dataframe, which contains various information for each of the 380 EPL games in the 201617 English Premier League season We restricted the dataframe to the columns in which we’re interested (specifically, team names and numer of goals scored by each team) I’ll omit most of the code that produces the graphs in this post But don’t worry, you can find that code on my github page Our task is to model the final round of fixtures in the season, so we must remove the last 10 rows (each gameweek consists of 10 matches) You’ll notice that, on average, the home team scores more goals than the away team This is the so called ‘home (field) advantage’ (discussed here) and isn’t specific to soccer This is a convenient time to introduce the Poisson distribution It’s a discrete probability distribution that describes the probability of the number of events within a specific time period (e g 90 mins) with a known average rate of occurrence A key assumption is that the number of events is independent of time In our context, this means that goals don’t become more/less likely by the number of goals already scored in the match Instead, the number of goals is expressed purely as function an average rate of goals If that was unclear, maybe this mathematical formulation will make clearer: represents the average rate (e g average number of goals, average number of letters you receive, etc ) So, we can treat the number of goals scored by the home and away team as two independent Poisson distributions The plot below shows the proportion of goals scored compared to the number of goals estimated by the corresponding Poisson distributions We can use this statistical model to estimate the probability of specfic events The probability of a draw is simply the sum of the events where the two teams score the same amount of goals Note that we consider the number of goals scored by each team to be independent events (i e P(A n B) = P(A) P(B)) The difference of two Poisson distribution is actually called a Skellam distribution So we can calculate the probability of a draw by inputting the mean goal values into this distribution So, hopefully you can see how we can adapt this approach to model specific matches We just need to know the average number of goals scored by each team and feed this data into a Poisson model Let’s have a look at the distribution of goals scored by Chelsea and Sunderland (teams who finished 1st and last, respectively) Building A Model You should now be convinced that the number of goals scored by each team can be approximated by a Poisson distribution Due to a relatively sample size (each team plays at most 19 home/away games), the accuracy of this approximation can vary significantly (especially earlier in the season when teams have played fewer games) Similar to before, we could now calculate the probability of various events in this Chelsea Sunderland match But rather than treat each match separately, we’ll build a more general Poisson regression model (what is that?) Generalized Linear Model Regression Results Dep Variable: goals No Observations: 740 Model: GLM Df Residuals: 700 Model Family: Poisson Df Model: 39 Link Function: log Scale: 1 0 Method: IRLS LogLikelihood: 1042 4 Date: Sat, 10 Jun 2017 Deviance: 776 11 Time: 11:17:38 Pearson chi2: 659 No Iterations: 8 coef std err z P>z [95 0% Conf Int ] Intercept 0 3725 0 198 1 880 0 060 0 016 0 761 team[T Bournemouth] 0 2891 0 179 1 612 0 107 0 641 0 062 team[T Burnley] 0 6458 0 200 3 230 0 001 1 038 0 254 team[T Chelsea] 0 0789 0 162 0 488 0 626 0 238 0 396 team[T Crystal Palace] 0 3865 0 183 2 107 0 035 0 746 0 027 team[T Everton] 0 2008 0 173 1 161 0 246 0 540 0 138 team[T Hull] 0 7006 0 204 3 441 0 001 1 100 0 302 team[T Leicester] 0 4204 0 187 2 249 0 025 0 787 0 054 team[T Liverpool] 0 0162 0 164 0 099 0 921 0 306 0 338 team[T Man City] 0 0117 0 164 0 072 0 943 0 310 0 334 team[T Man United] 0 3572 0 181 1 971 0 049 0 713 0 002 team[T Middlesbrough] 1 0087 0 225 4 481 0 000 1 450 0 568 team[T Southampton] 0 5804 0 195 2 976 0 003 0 963 0 198 team[T Stoke] 0 6082 0 197 3 094 0 002 0 994 0 223 team[T Sunderland] 0 9619 0 222 4 329 0 000 1 397 0 526 team[T Swansea] 0 5136 0 192 2 673 0 008 0 890 0 137 team[T Tottenham] 0 0532 0 162 0 328 0 743 0 265 0 371 team[T Watford] 0 5969 0 197 3 035 0 002 0 982 0 211 team[T West Brom] 0 5567 0 194 2 876 0 004 0 936 0 177 team[T West Ham] 0 4802 0 189 2 535 0 011 0 851 0 109 opponent[T Bournemouth] 0 4109 0 196 2 092 0 036 0 026 0 796 opponent[T Burnley] 0 1657 0 206 0 806 0 420 0 237 0 569 opponent[T Chelsea] 0 3036 0 234 1 298 0 194 0 762 0 155 opponent[T Crystal Palace] 0 3287 0 200 1 647 0 100 0 062 0 720 opponent[T Everton] 0 0442 0 218 0 202 0 840 0 472 0 384 opponent[T Hull] 0 4979 0 193 2 585 0 010 0 120 0 875 opponent[T Leicester] 0 3369 0 199 1 694 0 090 0 053 0 727 opponent[T Liverpool] 0 0374 0 217 0 172 0 863 0 463 0 389 opponent[T Man City] 0 0993 0 222 0 448 0 654 0 534 0 335 opponent[T Man United] 0 4220 0 241 1 754 0 079 0 894 0 050 opponent[T Middlesbrough] 0 1196 0 208 0 574 0 566 0 289 0 528 opponent[T Southampton] 0 0458 0 211 0 217 0 828 0 369 0 460 opponent[T Stoke] 0 2266 0 203 1 115 0 265 0 172 0 625 opponent[T Sunderland] 0 3707 0 198 1 876 0 061 0 017 0 758 opponent[T Swansea] 0 4336 0 195 2 227 0 026 0 052 0 815 opponent[T Tottenham] 0 5431 0 252 2 156 0 031 1 037 0 049 opponent[T Watford] 0 3533 0 198 1 782 0 075 0 035 0 742 opponent[T West Brom] 0 0970 0 209 0 463 0 643 0 313 0 507 opponent[T West Ham] 0 3485 0 198 1 758 0 079 0 040 0 737 home 0 2969 0 063 4 702 0 000 0 173 0 421 If you’re curious about the smf glm( ) part, you can find more information here (edit: earlier versions of this post had erroneously employed a Generalised Estimating Equation (GEE) what’s the difference?) I’m more interested in the values presented in the coef column in the model summary table, which are analogous to the slopes in linear regression Similar to logistic regression, we take the exponent of the parameter values A positive value implies more goals (), while values closer to zero represent more neutral effects () Towards the bottom of the table you might notice that home has a coef of 0 2969 This captures the fact that home teams generally score more goals than the away team (specifically, =1 35 times more likely) But not all teams are created equal Chelsea has a coef of 0 0789, while the corresponding value for Sunderland is 0 9619 (sort of saying Chelsea (Sunderland) are better (much worse!) scorers than average) Finally, the opponent* values penalize/reward teams based on the quality of the opposition This relfects the defensive strength of each team (Chelsea: 0 3036; Sunderland: 0 3707) In other words, you’re less likely to score against Chelsea Hopefully, that all makes both statistical and intuitive sense Let’s start making some predictions for the upcoming matches We simply pass our teams into poisson_model and it’ll return the expected average number of goals for that team (we need to run it twice we calculate the expected average number of goals for each team separately) So let’s see how many goals we expect Chelsea and Sunderland to score Predicting Football Results With Statistical Modelling Combining the world’s most popular sport with everyone’s favourite discrete probability distribution, this post predicts football matches using the Poisson distribution David Sheehan Data scientist interested in sports, politics and Simpsons references Football (or soccer to my American readers) is full of clichés: “It’s a game of two halves”, “taking it one game at a time” and “Liverpool have failed to win the Premier League” You’re less likely to hear “Treating the number of goals scored by each team as independent Poisson processes, statistical modelling suggests that the home team have a 60% chance of winning today” But this is actually a bit of cliché too (it has been discussed here, here, here, here and particularly well here) As we’ll discover, a simple Poisson model is, well, overly simplistic But it’s a good starting point and a nice intuitive way to learn about statistical modelling So, if you came here looking to make money, I hear this guy makes £5000 per month without leaving the house Poisson Distribution HomeTeam AwayTeam HomeGoals AwayGoals 0 Burnley Swansea 0 1 1 Crystal Palace West Brom 0 1 2 Everton Tottenham 1 1 3 Hull Leicester 2 1 4 Man City Sunderland 2 1 We imported a csv as a pandas dataframe, which contains various information for each of the 380 EPL games in the 201617 English Premier League season We restricted the dataframe to the columns in which we’re interested (specifically, team names and numer of goals scored by each team) I’ll omit most of the code that produces the graphs in this post But don’t worry, you can find that code on my github page Our task is to model the final round of fixtures in the season, so we must remove the last 10 rows (each gameweek consists of 10 matches) You’ll notice that, on average, the home team scores more goals than the away team This is the so called ‘home (field) advantage’ (discussed here) and isn’t specific to soccer This is a convenient time to introduce the Poisson distribution It’s a discrete probability distribution that describes the probability of the number of events within a specific time period (e g 90 mins) with a known average rate of occurrence A key assumption is that the number of events is independent of time In our context, this means that goals don’t become more/less likely by the number of goals already scored in the match Instead, the number of goals is expressed purely as function an average rate of goals If that was unclear, maybe this mathematical formulation will make clearer: represents the average rate (e g average number of goals, average number of letters you receive, etc ) So, we can treat the number of goals scored by the home and away team as two independent Poisson distributions The plot below shows the proportion of goals scored compared to the number of goals estimated by the corresponding Poisson distributions We can use this statistical model to estimate the probability of specfic events The probability of a draw is simply the sum of the events where the two teams score the same amount of goals Note that we consider the number of goals scored by each team to be independent events (i e P(A n B) = P(A) P(B)) The difference of two Poisson distribution is actually called a Skellam distribution So we can calculate the probability of a draw by inputting the mean goal values into this distribution So, hopefully you can see how we can adapt this approach to model specific matches We just need to know the average number of goals scored by each team and feed this data into a Poisson model Let’s have a look at the distribution of goals scored by Chelsea and Sunderland (teams who finished 1st and last, respectively) Building A Model You should now be convinced that the number of goals scored by each team can be approximated by a Poisson distribution Due to a relatively sample size (each team plays at most 19 home/away games), the accuracy of this approximation can vary significantly (especially earlier in the season when teams have played fewer games) Similar to before, we could now calculate the probability of various events in this Chelsea Sunderland match But rather than treat each match separately, we’ll build a more general Poisson regression model (what is that?) Generalized Linear Model Regression Results Dep Variable: goals No Observations: 740 Model: GLM Df Residuals: 700 Model Family: Poisson Df Model: 39 Link Function: log Scale: 1 0 Method: IRLS LogLikelihood: 1042 4 Date: Sat, 10 Jun 2017 Deviance: 776 11 Time: 11:17:38 Pearson chi2: 659 No Iterations: 8 coef std err z P>z [95 0% Conf Int ] Intercept 0 3725 0 198 1 880 0 060 0 016 0 761 team[T Bournemouth] 0 2891 0 179 1 612 0 107 0 641 0 062 team[T Burnley] 0 6458 0 200 3 230 0 001 1 038 0 254 team[T Chelsea] 0 0789 0 162 0 488 0 626 0 238 0 396 team[T Crystal Palace] 0 3865 0 183 2 107 0 035 0 746 0 027 team[T Everton] 0 2008 0 173 1 161 0 246 0 540 0 138 team[T Hull] 0 7006 0 204 3 441 0 001 1 100 0 302 team[T Leicester] 0 4204 0 187 2 249 0 025 0 787 0 054 team[T Liverpool] 0 0162 0 164 0 099 0 921 0 306 0 338 team[T Man City] 0 0117 0 164 0 072 0 943 0 310 0 334 team[T Man United] 0 3572 0 181 1 971 0 049 0 713 0 002 team[T Middlesbrough] 1 0087 0 225 4 481 0 000 1 450 0 568 team[T Southampton] 0 5804 0 195 2 976 0 003 0 963 0 198 team[T Stoke] 0 6082 0 197 3 094 0 002 0 994 0 223 team[T Sunderland] 0 9619 0 222 4 329 0 000 1 397 0 526 team[T Swansea] 0 5136 0 192 2 673 0 008 0 890 0 137 team[T Tottenham] 0 0532 0 162 0 328 0 743 0 265 0 371 team[T Watford] 0 5969 0 197 3 035 0 002 0 982 0 211 team[T West Brom] 0 5567 0 194 2 876 0 004 0 936 0 177 team[T West Ham] 0 4802 0 189 2 535 0 011 0 851 0 109 opponent[T Bournemouth] 0 4109 0 196 2 092 0 036 0 026 0 796 opponent[T Burnley] 0 1657 0 206 0 806 0 420 0 237 0 569 opponent[T Chelsea] 0 3036 0 234 1 298 0 194 0 762 0 155 opponent[T Crystal Palace] 0 3287 0 200 1 647 0 100 0 062 0 720 opponent[T Everton] 0 0442 0 218 0 202 0 840 0 472 0 384 opponent[T Hull] 0 4979 0 193 2 585 0 010 0 120 0 875 opponent[T Leicester] 0 3369 0 199 1 694 0 090 0 053 0 727 opponent[T Liverpool] 0 0374 0 217 0 172 0 863 0 463 0 389 opponent[T Man City] 0 0993 0 222 0 448 0 654 0 534 0 335 opponent[T Man United] 0 4220 0 241 1 754 0 079 0 894 0 050 opponent[T Middlesbrough] 0 1196 0 208 0 574 0 566 0 289 0 528 opponent[T Southampton] 0 0458 0 211 0 217 0 828 0 369 0 460 opponent[T Stoke] 0 2266 0 203 1 115 0 265 0 172 0 625 opponent[T Sunderland] 0 3707 0 198 1 876 0 061 0 017 0 758 opponent[T Swansea] 0 4336 0 195 2 227 0 026 0 052 0 815 opponent[T Tottenham] 0 5431 0 252 2 156 0 031 1 037 0 049 opponent[T Watford] 0 3533 0 198 1 782 0 075 0 035 0 742 opponent[T West Brom] 0 0970 0 209 0 463 0 643 0 313 0 507 opponent[T West Ham] 0 3485 0 198 1 758 0 079 0 040 0 737 home 0 2969 0 063 4 702 0 000 0 173 0 421 If you’re curious about the smf glm( ) part, you can find more information here (edit: earlier versions of this post had erroneously employed a Generalised Estimating Equation (GEE) what’s the difference?) I’m more interested in the values presented in the coef column in the model summary table, which are analogous to the slopes in linear regression Similar to logistic regression, we take the exponent of the parameter values A positive value implies more goals (), while values closer to zero represent more neutral effects () Towards the bottom of the table you might notice that home has a coef of 0 2969 This captures the fact that home teams generally score more goals than the away team (specifically, =1 35 times more likely) But not all teams are created equal Chelsea has a coef of 0 0789, while the corresponding value for Sunderland is 0 9619 (sort of saying Chelsea (Sunderland) are better (much worse!) scorers than average) Finally, the opponent* values penalize/reward teams based on the quality of the opposition This relfects the defensive strength of each team (Chelsea: 0 3036; Sunderland: 0 3707) In other words, you’re less likely to score against Chelsea Hopefully, that all makes both statistical and intuitive sense Let’s start making some predictions for the upcoming matches We simply pass our teams into poisson_model and it’ll return the expected average number of goals for that team (we need to run it twice we calculate the expected average number of goals for each team separately) So let’s see how many goals we expect Chelsea and Sunderland to score Predicting Football Results With Statistical Modelling Combining the world’s most popular sport with everyone’s favourite discrete probability distribution, this post predicts football matches using the Poisson distribution David Sheehan Data scientist interested in sports, politics and Simpsons references Football (or soccer to my American readers) is full of clichés: “It’s a game of two halves”, “taking it one game at a time” and “Liverpool have failed to win the Premier League” You’re less likely to hear “Treating the number of goals scored by each team as independent Poisson processes, statistical modelling suggests that the home team have a 60% chance of winning today” But this is actually a bit of cliché too (it has been discussed here, here, here, here and particularly well here) As we’ll discover, a simple Poisson model is, well, overly simplistic But it’s a good starting point and a nice intuitive way to learn about statistical modelling So, if you came here looking to make money, I hear this guy makes £5000 per month without leaving the house Poisson Distribution HomeTeam AwayTeam HomeGoals AwayGoals 0 Burnley Swansea 0 1 1 Crystal Palace West Brom 0 1 2 Everton Tottenham 1 1 3 Hull Leicester 2 1 4 Man City Sunderland 2 1 We imported a csv as a pandas dataframe, which contains various information for each of the 380 EPL games in the 201617 English Premier League season We restricted the dataframe to the columns in which we’re interested (specifically, team names and numer of goals scored by each team) I’ll omit most of the code that produces the graphs in this post But don’t worry, you can find that code on my github page Our task is to model the final round of fixtures in the season, so we must remove the last 10 rows (each gameweek consists of 10 matches) You’ll notice that, on average, the home team scores more goals than the away team This is the so called ‘home (field) advantage’ (discussed here) and isn’t specific to soccer This is a convenient time to introduce the Poisson distribution It’s a discrete probability distribution that describes the probability of the number of events within a specific time period (e g 90 mins) with a known average rate of occurrence A key assumption is that the number of events is independent of time In our context, this means that goals don’t become more/less likely by the number of goals already scored in the match Instead, the number of goals is expressed purely as function an average rate of goals If that was unclear, maybe this mathematical formulation will make clearer: represents the average rate (e g average number of goals, average number of letters you receive, etc ) So, we can treat the number of goals scored by the home and away team as two independent Poisson distributions The plot below shows the proportion of goals scored compared to the number of goals estimated by the corresponding Poisson distributions We can use this statistical model to estimate the probability of specfic events The probability of a draw is simply the sum of the events where the two teams score the same amount of goals Note that we consider the number of goals scored by each team to be independent events (i e P(A n B) = P(A) P(B)) The difference of two Poisson distribution is actually called a Skellam distribution So we can calculate the probability of a draw by inputting the mean goal values into this distribution So, hopefully you can see how we can adapt this approach to model specific matches We just need to know the average number of goals scored by each team and feed this data into a Poisson model Let’s have a look at the distribution of goals scored by Chelsea and Sunderland (teams who finished 1st and last, respectively) Building A Model You should now be convinced that the number of goals scored by each team can be approximated by a Poisson distribution Due to a relatively sample size (each team plays at most 19 home/away games), the accuracy of this approximation can vary significantly (especially earlier in the season when teams have played fewer games) Similar to before, we could now calculate the probability of various events in this Chelsea Sunderland match But rather than treat each match separately, we’ll build a more general Poisson regression model (what is that?) Generalized Linear Model Regression Results Dep Variable: goals No Observations: 740 Model: GLM Df Residuals: 700 Model Family: Poisson Df Model: 39 Link Function: log Scale: 1 0 Method: IRLS LogLikelihood: 1042 4 Date: Sat, 10 Jun 2017 Deviance: 776 11 Time: 11:17:38 Pearson chi2: 659 No Iterations: 8 coef std err z P>z [95 0% Conf Int ] Intercept 0 3725 0 198 1 880 0 060 0 016 0 761 team[T Bournemouth] 0 2891 0 179 1 612 0 107 0 641 0 062 team[T Burnley] 0 6458 0 200 3 230 0 001 1 038 0 254 team[T Chelsea] 0 0789 0 162 0 488 0 626 0 238 0 396 team[T Crystal Palace] 0 3865 0 183 2 107 0 035 0 746 0 027 team[T Everton] 0 2008 0 173 1 161 0 246 0 540 0 138 team[T Hull] 0 7006 0 204 3 441 0 001 1 100 0 302 team[T Leicester] 0 4204 0 187 2 249 0 025 0 787 0 054 team[T Liverpool] 0 0162 0 164 0 099 0 921 0 306 0 338 team[T Man City] 0 0117 0 164 0 072 0 943 0 310 0 334 team[T Man United] 0 3572 0 181 1 971 0 049 0 713 0 002 team[T Middlesbrough] 1 0087 0 225 4 481 0 000 1 450 0 568 team[T Southampton] 0 5804 0 195 2 976 0 003 0 963 0 198 team[T Stoke] 0 6082 0 197 3 094 0 002 0 994 0 223 team[T Sunderland] 0 9619 0 222 4 329 0 000 1 397 0 526 team[T Swansea] 0 5136 0 192 2 673 0 008 0 890 0 137 team[T Tottenham] 0 0532 0 162 0 328 0 743 0 265 0 371 team[T Watford] 0 5969 0 197 3 035 0 002 0 982 0 211 team[T West Brom] 0 5567 0 194 2 876 0 004 0 936 0 177 team[T West Ham] 0 4802 0 189 2 535 0 011 0 851 0 109 opponent[T Bournemouth] 0 4109 0 196 2 092 0 036 0 026 0 796 opponent[T Burnley] 0 1657 0 206 0 806 0 420 0 237 0 569 opponent[T Chelsea] 0 3036 0 234 1 298 0 194 0 762 0 155 opponent[T Crystal Palace] 0 3287 0 200 1 647 0 100 0 062 0 720 opponent[T Everton] 0 0442 0 218 0 202 0 840 0 472 0 384 opponent[T Hull] 0 4979 0 193 2 585 0 010 0 120 0 875 opponent[T Leicester] 0 3369 0 199 1 694 0 090 0 053 0 727 opponent[T Liverpool] 0 0374 0 217 0 172 0 863 0 463 0 389 opponent[T Man City] 0 0993 0 222 0 448 0 654 0 534 0 335 opponent[T Man United] 0 4220 0 241 1 754 0 079 0 894 0 050 opponent[T Middlesbrough] 0 1196 0 208 0 574 0 566 0 289 0 528 opponent[T Southampton] 0 0458 0 211 0 217 0 828 0 369 0 460 opponent[T Stoke] 0 2266 0 203 1 115 0 265 0 172 0 625 opponent[T Sunderland] 0 3707 0 198 1 876 0 061 0 017 0 758 opponent[T Swansea] 0 4336 0 195 2 227 0 026 0 052 0 815 opponent[T Tottenham] 0 5431 0 252 2 156 0 031 1 037 0 049 opponent[T Watford] 0 3533 0 198 1 782 0 075 0 035 0 742 opponent[T West Brom] 0 0970 0 209 0 463 0 643 0 313 0 507 opponent[T West Ham] 0 3485 0 198 1 758 0 079 0 040 0 737 home 0 2969 0 063 4 702 0 000 0 173 0 421 If you’re curious about the smf glm( ) part, you can find more information here (edit: earlier versions of this post had erroneously employed a Generalised Estimating Equation (GEE) what’s the difference?) I’m more interested in the values presented in the coef column in the model summary table, which are analogous to the slopes in linear regression Similar to logistic regression, we take the exponent of the parameter values A positive value implies more goals (), while values closer to zero represent more neutral effects () Towards the bottom of the table you might notice that home has a coef of 0 2969 This captures the fact that home teams generally score more goals than the away team (specifically, =1 35 times more likely) But not all teams are created equal Chelsea has a coef of 0 0789, while the corresponding value for Sunderland is 0 9619 (sort of saying Chelsea (Sunderland) are better (much worse!) scorers than average) Finally, the opponent* values penalize/reward teams based on the quality of the opposition This relfects the defensive strength of each team (Chelsea: 0 3036; Sunderland: 0 3707) In other words, you’re less likely to score against Chelsea Hopefully, that all makes both statistical and intuitive sense Let’s start making some predictions for the upcoming matches We simply pass our teams into poisson_model and it’ll return the expected average number of goals for that team (we need to run it twice we calculate the expected average number of goals for each team separately) So let’s see how many goals we expect Chelsea and Sunderland to score surefixedmatch club/footballtipsblog Football tips blog surefixedmatch club/fcsoccerprediction Fc soccer prediction surefixedmatch club/topsportbets Top sport bets surefixedmatch club/propickscollegebasketball Pro picks college basketball surefixedmatch club/serieacorrectscoreprediction Serie a correct score prediction surefixedmatch club/eplaccumulator Epl accumulator surefixedmatch club/superrugbybettingpreview Super rugby betting preview surefixedmatch club/ncaacollegebasketballpicksagainstthespread Ncaa college basketball picks against the spread surefixedmatch club/fixedsoccermatchesreddit Fixed soccer matches reddit surefixedmatch club/sbrbasketballpicks Sbr basketball picks surefixedmatch club/nbcsportscollegefootballpicks Nbc sports college football picks surefixedmatch club/sportsbookcalifornia Sportsbook california surefixedmatch club/doublechancefootballpredictions Double chance football predictions surefixedmatch club/week10nflfantasypicks Week 10 nfl fantasy picks surefixedmatch club/nfldraftpicktracker Nfl draft pick tracker surefixedmatch club/sureoddstips Sure odds tips surefixedmatch club/week3collegepicksagainstspread Week 3 college picks against spread surefixedmatch club/cbspicksagainstthespread Cbs picks against the spread surefixedmatch club/nhlfirstrounddraftpicks2020 Nhl first round draft picks 2020 surefixedmatch club/nfl3rdweekpicks Nfl 3rd week picks surefixedmatch club/week2survivorfootballpicks Week 2 survivor football picks surefixedmatch club/soccer6resultsandpayoutssoccer10 Soccer 6 results and payouts soccer 10 surefixedmatch club/angelsdraft2020 Angels draft 2020 surefixedmatch club/soccerlasvegasodds Soccer las vegas odds surefixedmatch club/sportsbettinglegalcountries Sports betting legal countries surefixedmatch club/nhlbettingtipstoday Nhl betting tips today surefixedmatch club/onlinebettingnetherlands Online betting netherlands surefixedmatch club/bookingpointssportpesa Booking points sportpesa surefixedmatch club/aleaguefantasytips A league fantasy tips surefixedmatch club/nflfirstweekodds Nfl first week odds surefixedmatch club/reziltabettingtips Rezilta betting tips surefixedmatch club/nbaoddswinner Nba odds winner surefixedmatch club/waiverwireweek62020 Waiver wire week 6 2020 surefixedmatch club/thelogicofsportsbetting The logic of sports betting surefixedmatch club/fixedmatchesotzyvy Fixed matches отзывы surefixedmatch club/2004wnbadraft 2004 wnba draft xnblq298bhi0b hznano com/space php?uid=12532 Winning Predict Today Sure boyar kiev ua/index php?/user/46309wonunwibre/ Uk England Fixed Matches zandroid com/engine/ajax/addcomments php what does correct score any other mean bonsaiforums com/user/Wongaisee via correct score apk hack bd24news com/author/wonpipse/ 100 FOOTBALL PREDICTIONS 1×2 forum shopheroesgame com/viewtopic php?f=4&t=229435 u bet correct score forums uosecondage com/memberlist php?mode=viewprofile&u=16919 turkey fixed matches yahoo com amf cx/UserWonslunc Today Win Predictions Site forum hangarnet com br/member php?action=profile&uid=82600 Today Strong Football Tips abactoday com/members/wonveste/profile/ zarabet fixed matches forum kraspivo ru/viewtopic php?f=11&t=112851 uk sure fixed matches baseballbats net/forums/member php?34798WonAuthome xanthi correct score forchess ru/member php?u=91940 Ukraine Fixed Matches manymen men/?action=post;board=11 0 Vip Betting Tips Fixed heroscapers com/community/member php?u=269919 Top Bankers Today Sure clm disnaikid com/space php?uid=9060 Winning Fixed Match Soccer diendanmevabe com/members/520377WonSwifimb html what is correct score raritetno com/user/Wongrothe/ the correct score tips apk rutalk co uk/member php?583875Wonauthems x1 fixed matches madpride org uk/forum/memberlist php?mode=viewprofile&u=172570&sid=015fa4962890f6b0 7aa8c033ff25df97 Sweden Best Fixed Matches Today sportfanszone com/?post_type=topic&p=12536 Sure Win Betting Predict forum grafol ru/memberlist php?mode=viewprofile&u=82754 Tips Free Today zxymk com/spaceuid175624 html 1 fixed matches forum thatchedgroup com/memberlist php?mode=viewprofile&u=209723 Top Fixed Matches Tips antfarm ru/user/WonHanuap/ Today Match Prediction Sure 

04.06.2021, 03:58  #19506 (permalink)  

correct score calculator
cutt ly/tgUsZ9U i ibb co/rbgFnyV/fixedmatches jpg
bit ly/3dWKdMz i ibb co/hHjZkcb/VISITSITENOW png Correct Score Betting Calculator Correct score betting calculator is available below You can use it to calculate border odds for goal outcome sports betting and to predict match scores and goal amounts for an event Meanwhile, please register to one of our featured sportsbooks below to support our site: Input team goal averages and you will get correct score border odds (up to 1000x) showing Calculator is useful in sports where there are usually less than 10 goals scored per team in a match These sports include ice hockey and football for example Chances of Goals Goals Home Visitor Probabilities 1 X 2 0% 0% 0% Scores Score Chance Border Copyright © Winner Gambling Powered by WP Test Grade Calculator If you're looking for a tool which can help you in setting a grading scale, this test grade calculator is a must Also known as test score calculator or teacher grader , this tool quickly finds out the grade and percentage on the basis of the number of points and wrong (or correct) answers Moreover, you can change the default grading scale and set your own one Ar you still wondering how to calculate test score? Scroll down to find out  or simply experiment with this grading scale calculator If this test grade calculator is not the tool you're exactly looking for, check out our other grading calculators like the high school GPA calculator with many weighting options, as well as the complementary college GPA calculator Also, you may find the final grade tool useful to check what your final grade will be  or what you can do to improve it Besides, if you are considering to take a student loan, check out our student loan calculator where you can make a projection on your expenses and study the effect of different student loan options on your budget How to calculate test score To calculate the percentile test score, all you need to do is divide the earned points by the total points possible In other words, you're simply finding the percentage of good answers: percentage score = #correct / #total percentage score = (#total  #wrong) / #total Then, all you need to do is convert the percentage score into a letter grade The default grading scale looks as in the table below: If you don't using the +/ grades, the scale may look like: An A is 90% to 100% A B is 80% to 89% A C is 70% to 79% A D is 60% to 69% and finally an F is 59% and below  and it's not a passing grade Above you could find the standard grading system for US schools and universities However, the grading may vary among schools, classes and teachers Always check beforehand which system is used in your case Sometimes the border of passing score is not 60%, but e g 50 or 65% What then? We've got you covered  you can change the ranges of grades! Read more about it in the last paragraph of this article: Advanced mode options Test grade calculator  how to use it? Our test score calculator is a straightforward and intuitive tool! Enter the number of questions/points/problems in the student's work (test, quiz, exam  anything) Assume you've prepared the test with 18 questions Type in the number the student got wrong Instead  if you prefer  you can enter the number of gained points Let's say our exemplary student failed to answer three questions Here we go! Teacher grader tool is showing the percentage and grade for that score For our example, the student got a score of 83 33% from a test, which corresponds to B grade Underneath you'll find a full grading scale table So to check the score for the next students, you can type in the number of questions they've got wrong  or just use this neat table Test grade calculator  advanced mode options That was a basic version of the calculations But our teacher grader is a much more versatile and flexible tool! You can choose more options to customize this test score calculator Just hit the Advanced mode button below the tool, and two more options will appear: Increment by box  here you can change the look of the table which you get as a result The default value is 1, which means that the student can get an integer number of points But sometimes it's possible to get, e g halfpoints  then you can use this box to declare the increment between next scores Percentage scale  in that set of boxes, you can change the grading scale from the default one For example, assume that the test was really difficult and you'd like to change the scale so that getting 50% is already a passing grade (usually it's 60% or even 65%) Change the last box Grade D ≥ value from default 60% to 50% to reach the goal You can also change the other ranges if you want to And what if I don't need +/ grades ? Well, then just ignore the signs Tools & Calculators The Albert Team Last Updated On: September 21, 2020 Are you taking the SAT® exam soon and not sure how you might do? Then you’re at the right place! With this interactive SAT® score calculator, you can predict how your raw score translates to your SAT® score to answer the common question, “Is my SAT® score good enough?” If you’re looking for free help as you start your SAT® test prep, be sure to explore our SAT® sections for more review articles ( Math , Reading , Writing ) If you’re an educator interested in boosting your SAT® scores, let us know and we’ll tell you how you can start using Albert for free SAT® Score Calculator Enter your scores Results SAT® Reading Section SAT® Writing Section SAT® Math Section SAT® Reading Section SAT® Writing Section SAT® Math Section SAT® Reading Section SAT® Writing Section SAT® Math Section SAT® Reading Section SAT® Writing Section SAT® Math Section SAT® Reading Section SAT® Writing Section SAT® Math Section Choose your score curve Did you find this helpful? Click here to share this calculator on Twitter Looking for SAT® study materials? How do you calculate SAT® scores? When the SAT® revamped in March of 2016, scores became easier to calculate The test went back to being scored out of a total possible 1600 points When calculating your SAT® score, there are a few key components: First, there is your reading test raw score This raw score is equivalent to the number of SAT® Reading questions you get correct on the test (there are 52 in total) From your raw score, a Reading Test Score is calculated between 1040 Next, there is your writing and language test raw score This is equal to the number of questions you get right out of the 44 questions in this section From your raw score, a Writing and Language Test Score is calculated between 1040 Adding your Reading Test Score and Writing and Language Test Score becomes your Reading and Writing Test Score (which ranges from 2080) This number is multiplied by 10 to get your EvidenceBased Reading and Writing Section Score (between 200800) Finally, there is your math score For this section, you add the raw score (the number of correct answers) from both the no calculator and calculator sections to get your math section raw score This is then converted using a scoring chart to output your Math Section Score (between 200800) This means your total SAT® score can range from 4001600 What’s the difference between SAT® raw scores and SAT® scale scores? How are they calculated? As noted in the prior question, SAT® raw scores are equivalent to the number of correct answers you got in a section The SAT® does not have a guessing penalty and only cares about the total number of correct answers SAT® scale scores are how your raw scores translate when converted to section scores — these are between 200800 for the two sections (EvidenceBased Reading and Writing and Math), to give you a total SAT® score between 4001600 What is a good SAT® score? Decent score? Bad score? A good SAT® score really depends on the student and their aspirations For example, if you’re applying to Harvard and have a 1200 SAT® score, it’s unlikely you’ll get in since Harvard’s average score is typically over 1500 That being said, if you’re applying to Michigan State University with that same score, that would be competitive for your college application Generally, in our opinion, anything that falls into the top 30% of graduating high school students should be considered a good SAT® score When you review the 2019 SAT® score trends , you see the nationally representative sample average SAT® score is 1120 The 70th percentile SAT® test taker is 1170 The former number compares how students did on the SAT® to an overall sample of all students grades 1112, regardless of whether or not they took the SAT® The latter number applies the actual scores of students in the past three graduating classes to the latest SAT® A decent SAT® score would probably be something around the 50th percentile Using the nationally representative sample, you’d find this to be a 1010 Looking at just SAT® test takers, the 50th percentile SAT® score would be between a 1050 and 1060 A bad SAT® score is quite subjective, but if you were looking at it from a percentiles standpoint, it could be any score below the 25th percentile Looking at the nationally representative sample, this is between 870 and 880 For just SAT® test takers, it’d be a 910 Is 1600 a good SAT® score? Yes! A 1600 is not just a good SAT® score, it’s a perfect SAT® score Just like the ACT®, depending on the particular test, there is sometimes leeway on how to get a perfect SAT® score In other words, there are edge cases where you may be able to get one Reading question wrong and still get an 800 for your EvidenceBased Reading and Writing Section Score How hard is it to get a 1400 on the SAT®? It can be pretty tough to score a 1400 on the SAT® Scoring a 1400 means you’re in the 97th percentile for the nationally representative sample and the 94th percentile among SAT® test takers Furthermore, if you were to assume you wanted to score a 700 in both sections and you play around with the score calculator above, you’d see that to score a 700 in math, you can only miss around eight questions on average Then, to score a 700 in EvidenceBased Reading and Writing, you’d only be able to miss around eight questions in SAT® Reading and five questions in SAT® Writing Is 1200 a good SAT® score? A 1200 is a good SAT® score When you review the 2019 SAT® score trends, you’d see that a 1200 equates to the 81st percentile for the nationally representative sample, and 74th percentile for SAT® test takers This means scoring a 1200 on the SAT® puts you in the top quartile of high school students taking the test What is the average SAT® score? The average SAT® score is typically between 1010 and 1060 This is pulled from the SAT® score trend data in which the 50th percentile for the nationally representative sample was a 1010, and among SAT® test takers, the 50th percentile fell between a 1050 and 1060 Why is the SAT® exam curved? The SAT® exam itself is not curved relative to test takers That being said, the College Board does put each test through a process referred to as equating This process ensures no student receives an advantage or disadvantage from taking a particular for on the SAT® on a particular day In other words, it ensures a test score of 500 equals a test score 500 on an SAT® from another day The equating process is also why you’ll notice that when you use our SAT® score calculator, there are sometimes variances in how you might have scored on one practice test versus another There can be cases for instance where getting a perfect score in Math was necessary for an 800, while you could get one question wrong in another Since the last SAT® change in March 2016, the SAT® has remained consistent in terms of how raw scores translate to scale scores How do I read my SAT® Score Report? The College Board provides a helpful short video on how to understand your SAT® score report here Upon logging in, you’ll see your total SAT® score, which combines your EvidenceBased Reading and Writing Section and Math Section score In your SAT® Score Report, you’ll also find specifics on your test scores (number correct and incorrect in each section), crosstest scores (how you analyze texts and solve problems that are interdisciplinary with Science and History) and subscores (how you performed on specific key concepts) These sections will be color coded so you know exactly where you need to improve If you took the essay, you’ll see how you did on reading, writing, and analysis If you prefer not watching a video on this, you can review the College Board’s PDF resource on reading SAT® Score Reports here Why should I use this SAT® score calculator? Albert’s SAT® score calculator uses official practice test curves from the College Board This means our calculations are accurate and uptodate to the practice materials shared from the test maker If you’re ever in doubt and would like to confirm the score conversion charts for yourself, you can review the official resources here We made this SAT® score calculator because we saw that everyone else simply replicated the tables when creating what they called a “calculator” Interactive score calculators with sliders are a way more visual and fun way to motivate yourself to preparing for your SAT® They help you actually play with levers on what sections you could see the biggest boost in your score from to get your desired SAT® score How do you figure out your SAT® superscore? To figure out your SAT® superscore, you’ll need to first compile all of the test days you took the SAT® Next, look for your highest scores for SAT® EvidenceBased Reading and SAT® Math So for example, if you got a 700 on one SAT® Math test, and a 750 on another, you’d choose the 750 Finally, total your highest scores — this is your SAT® superscore Looking for extra SAT® practice? Albert provides hundreds of SAT® practice with detailed explanations and fulllength practice tests MCAT Score Conversion Calculator  AAMC Practice Exam The AAMC made this decision due to the COVID19 pandemic, and to allow for three different times per test date (6:30 AM, 12:15 PM, and 6:00 PM) Examinees will still be tested on content from all four sections of the exam, and will still be scored from 472 to 528 Since the number of questions in each section has to been reduced, students should take the AAMC practice exams in the same shortened format Then enter the number of correctly answered questions you received, and we’ll share your simulated score! First, find out which AAMC questions you can skip to simulate the shortened exam Select the AAMC fulllength practice exam you plan to take Below are the questions you can skip to simulate the shortened exam Correct Score Prediction & Correct Score Tips For Today & Tomorrow As an expert correct score prediction site we recommend various prediction and tips for both today and tomorrow ⚽ Football 🎾 Tennis 🎮 eSports Correct Score for today Brazil  Serie A Correct Score ✔️ Expires in 14 hrs ✔️ 37 Voucher used ✔️ 22Bet  💎 Best Bookmaker ✔️ Sports, eSports, Casino Colombia  Primera A Correct Score Cyprus  First Division Correct Score ✔️ Expires in 14 hrs ✔️ 38 Voucher used ✔️ 1xBet  💎 Best Bookmaker ✔️ Sports, eSports, Casino Denmark  Superliga Correct Score England  Premier League Correct Score Greece  Greek Cup Correct Score Italy  Serie C  Group B Correct Score Portugal  Primeira Liga Correct Score Romania  Liga 1 Correct Score South Africa  Nedbank Cup Correct Score Switzerland  Super League Correct Score Turkey  Super Lig Correct Score World  FIFA Club World Cup Correct Score ✔️ Expires in 14 hrs ✔️ 38 Voucher used ✔️ 1xBet  💎 Best Bookmaker ✔️ Sports, eSports, Casino Correct Score Prediction & Correct Score Tips For Today & Tomorrow Feedinco offers 100 correct score prediction for both Today and tomorrow with special focus from our Team experts for the best correct score prediction result These can be played directly with Bet365 correct score, betway correct score and lot more of bookmakers These exact score tips are offered for all major big leagues with four football prediction tips each sure correct score So what is the difference between Correct Score Prediction & Correct Score Tips? Nothing, since referring to the same thing Both suggest the same outcome with the Correct score As the title says, Correct Score Prediction & Correct Score Tips, this website offers correctscores tips daily correct score tips today correct score prediction free today correct score tips correct score tips correct score prediction Feedinco is always trying to give the best tipster which in return offer free exact score tips These prediction for correct scores today can be used with any bookmaker site, by using our betslip generation (clicking on the odds button) These correct score tips daily are as said free to use, being a sure correct score site! To navigate between the correct score of today and correct score tomorrow all you need to do is press on the top day filters All football correct score tips and ht correct score tips are found in out tips page where one can check the statistical analysis of both teams Feedinco is now considered to give the best correct score prediction / best site for correct score All football correct score prediction is free to use! Feedinco is trying to be the Best correct score prediction site to offer various fixed matches as free correct score With these fixed correct scores for today, one can win Big! Correct Score Prediction Feedinco is now offering 100 correct score prediction free and became the correct score predictions sites which punters want to have All the correct score tips above are all free exact score tips All best tipster correct score is reviewed by our experts and done with mathematical analysis from previous matches and H2h analysis We try our best to give the best correct score prediction Correct Score Feedinco is now one of the best betting tips sites you can find With the daily betting tips you can create your betting tips list from the list above and create a bet tip win so that you can win Big! These correct score are offered daily so if you want correct score tomorrow all you need to do is select the preferred day! The best football tips for today are always available for free, everyday! These online betting tips in this hot prediction site All accurate football prediction found here are given correct score prediction also with 4 other betting tips So to make a best football prediction site free you must be included in the top soccer prediction sites All today match prediction and real football prediction are all given for Free If you want the paid betting tips please visit our paid betting tips which are given daily and odds of around 2 00 The best way to understand Correct score, this research paper cover Prediction for the outcome of soccer matches What are Correct Score Predictions? correct score predictions bets are played dry The player indicates the exact number of goals that both teams will score in the course of ninety regular minutes Certainly a very dangerous game in the eyes of an expert The professional player knows correct score tips are a great asset if well placed We basically have two different approaches The first approach is mathematical, through the use of mathematical rules and formulas to create game systems that bring the player to the cashier We can also call it the most nerdy approach That point of view that only a fan tends to exploit The second approach is a little less mathematical but certainly no less methodical You make predictions based on your knowledge of the teams that will take the field It is an approach that relies heavily on factor c, which is luck In the first case, however, what the player tries to do is eliminate luck as much as possible to try to be certain on the correct score surefixedmatch com 666 fixed matches surefixedmatch club/nhlpreseasonpickstonight Nhl preseason picks tonight surefixedmatch club/skysportspredictionsleague1 Sky sports predictions league 1 surefixedmatch com unogoal 1 fixed matches surefixedmatch club/timedoutloadingsessiongta5ps3 Timed out loading session gta 5 ps3 surefixedmatch club/2020fantasyfootballtips 2020 fantasy football tips surefixedmatch club/100percentsurewinstoday 100 percent sure wins today surefixedmatch club/nflweek7survivorpool Nfl week 7 survivor pool surefixedmatch club/sanmanuelsportsbetting San manuel sports betting surefixedmatch club/skybettipshorseracing Sky bet tips horse racing surefixedmatch club/paddypowerrugbyunion Paddy power rugby union surefixedmatch club/2008majorleaguebaseballdraft 2008 major league baseball draft surefixedmatch club/listoffirstoverallnhldraftpicks List of first overall nhl draft picks surefixedmatch club/bestbttspredictionstoday Best btts predictions today surefixedmatch club/predictionsitefordrawmatches Prediction site for draw matches surefixedmatch club/ufc231draftkingspicks Ufc 231 draftkings picks surefixedmatch club/ncaabasketballbracketpredictions2020 Ncaa basketball bracket predictions 2020 surefixedmatch club/tiparenafixedmatches Tip arena fixed matches surefixedmatch com correct score 2 away explained surefixedmatch club/ncaafpickspapa Ncaaf picks papa surefixedmatch club/cbssportsgolfpicks Cbs sports golf picks surefixedmatch club/draftkingswnbalineup Draftkings wnba lineup surefixedmatch club/bestbetinhorseracingtoday Best bet in horse racing today surefixedmatch club/nbadraftlotterypicks2020 Nba draft lottery picks 2020 surefixedmatch club/collegefootballbowlpredictionsbleacherreport College football bowl predictions bleacher report surefixedmatch club/bestoddstowinweek1nfl Best odds to win week 1 nfl surefixedmatch club/sportsbookjobs Sportsbook jobs surefixedmatch club/nfllinesweek22020 Nfl lines week 2 2020 surefixedmatch club/delawareparksportsbookhours Delaware park sports book hours surefixedmatch club/espnfootypicks Espn footy picks surefixedmatch club/nflmondaypicks Nfl monday picks surefixedmatch club/onlinemeridianbetting Online meridian betting surefixedmatch club/soccerandfootballpredictionstomorrow Soccer and football predictions tomorrow surefixedmatch club/eachwaybetfootball Each way bet football surefixedmatch club/week15fantasyfootballwaiverwire Week 15 fantasy football waiver wire wispinternet com/legacyguild co uk/forum/memberlist php?mode=viewprofile&u=176334 Europe UEFA FIXED MATCHES forum kraspivo ru/viewtopic php?f=11&t=112938 europa fixed games forum xbadm ru/member php?u=87453 fc fixed matches 100 zxymk com/spaceuid175624 html fixed games free baseballbats net/forums/member php?34798WonAuthome Fixed Games 1×2 sc2rep ru/f/memberlist php?mode=viewprofile&u=34264 estonia fixed games foro viajarafrancia com/member92544 html?sid=0e27e481fb4a97d302df3f3eff02c29f fixed games 100 zandroid com/engine/ajax/addcomments php fc fixed matches tips forum aunbox com/member php?3144728Woniniste Expert Correct Score Prediction gearnation bigearssolutions ph/member php?action=profile&uid=2072 fixed games in football radiorecord fm/user/WonPaype/ England Football Betting Tips forums bsdinsight com/resources/authors/wonenfots 69728/ England Soccer Games forum vina ir/member php?action=profile&uid=66646 fifa 20 fixed games forum ladypopular com/viewtopic php?f=18&t=171496 Extra Fixed Matches Tips bonsaiforums com/user/Wongaisee expert 666 fixed matches abactoday com/members/wonveste/profile/ fixed games betting tips rutalk co uk/member php?583875Wonauthems fixed games 1x2 forchess ru/member php?u=91940 europe fixed games 

04.06.2021, 04:47  #19508 (permalink)  

Hot photo galleries blogs and pictures
New project started to be available today, check it out
Pornstar Galleries old porn hometown honies records of porn cartoon porn videos mbc porn star israel priscilla aka pics porn 2 Dirty Porn Photos, daily updated galleries Sexy porn tube porn star soccer bi sexual porn tubes sickest porn animal porn hub mature woman masturbates free granny secretry porn movie 3 New super hot photo galleries, daily updated collections Super Porn Pics  Free XXX pictures free high definition porn clips you porn interacial pale solo porn free dirtiest porn videos big brother ex housmates making porn 5 Girls of Desire: All babes in one place, crazy, art http://cabin.john.handjob.porn.lexixxx.com/?ally heshe in the shower porn stacking porn videos ph porn heather borgman porn bbq porn on imagefap 8 Sexy photo galleries, daily updated pics Free porn tube site abby pure 18 tingling sensation porn bleach halibel porn fream movies porn dyke lesbian porn made by women porn harrd 8 New project started to be available today, check it out Nappanee hot porn position ai no pai porn vanity tranny porn stars miniapolis porn shops foreign exchange harmony uk porn denisevf4; 04.06.2021 04:56. : 

04.06.2021, 06:13  #19509 (permalink)  

fixed matches real
cutt ly/tgUsZ9U i ibb co/rbgFnyV/fixedmatches jpg
bit ly/3dWKdMz i ibb co/qxX6sQj/olujak69 png What Are Fixed Matches And The Real Truth About Fixed Matches! What is a fixed match(official): In organized sports, match fixing occurs as a match is played to a completely or partially Predetermined result, violating the rules of the game and often the law Matchfixing , when motivated by gambling, requires contacts (and normally money transfers) between gamblers, players, team officials, and/or referees Match – fixing is the process of intentionally losing a game, through players performing poorly on purpose or corrupt officials As sports betting has become more detailed and exotic, spot fixing has risen Unlike match – fixing , this involves fixing small aspects of games, often inconsequential to the end result What do the experts say about fixed matches? Whether it’s fixed by both teams, a referee taking a bribe favoring one side over another or even by a single player in one team the ending result is known by the fixers The tipsters that provide such matches can be divided into two groups – scammers that will give fake stats, give false promises for sure wins and get rich quick schemes and those that put in the hours of hard work to provide a real service The Real Truth About Fixed Matches Is There Are Extremely Hard To Secure And Find! When it comes to paying for information, it is always a tricky situation and even more so in the betting world And so called tipsters are sending free fixed matches sometimes So someone offered you free fixed matches with no payment? You Gotta Trust them don’t you? But if you believe us, we guarantee you that they are all scammers We will give you indepth details about how actual match fixing works If you don’t want to read further, then here is the entire post summary in a single line – Free Fixed Matches Predictions Are All Scam ! This Is The Real Truth About Fixed Matches! (For Free) CONCLUSION Now to the conclusion of the topic: The Real Truth About Fixed Matches! The real truth about fixed matches is that: Yes, there are legit fixed matches, no doubt about that! But, finding one online is a road full of failure because if they were easy to find the odds would drop and there would be no profit in them, not to mention that the authorities would find out You should be careful who you trust, fixed matches are very expensive and tipsters giving fixed matches for a ridiculous amount like 50$100$ is most likely nothing more than a prediction! We keep them to ourselves to maximize profits without gaining publicity or doubt We only trust our fixed matches to real investors and veterans of top100soccersites Real Fixed Match Real Fixed Matches Real Fixed Matches We are a team of professionals on a high level tipping experience, a team who guaranteed profit with truly real free football and soccer tips, fixed matches and best predictions on this field, where you can get tips with real score We guess more of 95% of our football matches with a really great prices We have a lot of cooperators all around the world If you want to increase your profits, you are in the right place Our sources are reliable and affordable, always on time to represent you free betting tips worldwide ATTENTION: Our team for support will never reply you if in your message have any of this words: pay after win, trial matches or free matches Fixed Matches Half Time Full Time Fixed Matches Half Time Full Time HTFT Fixed matches Buy our 100% sure halftime fulltime tips and make good profit Just follow our instructions, and see how your income grows rapidly, day by day! NEXT MATCHES AVAILABLE ON 06 02 2021 SATURDAY (100% SURE FIXED MATCHES) Multiples Fixed Matches Half Time Full Time Odds about 600/900 00 100% Guaranteed Fixed Matches Match: – Tip: 2/1 (half timefull time) Odd: 29 00 And Match: – Tip: 1/2 (half timefull time) Odd: 29 00 QUESTION: After the payment is done, in how much time I will receive the games? As said before after the payment procedure is completed, it takes few minutes to verify the payment Our sales manager review and verify the payment and after they confirm they will send the tips immediately However, it can not take much more than 30 minutes Fixed Matches Archives Fixed Matches Archives LAST FIXED MATCHES 03 02 2021 DATE MATCHES TIP ODD FT W/L 03 02 2021 Hamilton – Ross County 1/2 26 00 1:0 / 1:2 03 02 2021 Aalborg – FC Copenhagen 1/2 21 00 2:0 / 2:3 CLICK ON PRICTURE TO VISIT PROOF FROM OUR BET AND WHAT'S APP (03 02 2021 Wednesday Proofs) LAST FIXED MATCHES 30 01 2021 DATE MATCHES TIP ODD FT W/L 30 01 2021 Entella – Cosenza 1/2 29 00 1:0 / 1:2 30 01 2021 Boreham Wood – Eastleigh 1/2 34 00 1:0 / 1:2 CLICK ON PRICTURE TO VISIT PROOF FROM OUR BET AND WHAT'S APP (30 01 2021 Saturday Proofs) LAST FIXED MATCHES 23 01 2021 DATE MATCHES TIP ODD FT W/L 23 01 2021 AlNajma – AlMuharraq 2/1 34 00 0:1 / 2:1 23 01 2021 Viktoria Koln – Mannheim 1/2 23 00 1:0 / 1:2 CLICK ON PRICTURE TO VISIT PROOF FROM OUR BET AND WHAT'S APP (23 01 2021 Saturday Proofs) Real Fixed Correct Score Real Fixed Correct Score When it comes to soccer betting tips, Football correct score tips is the leader in the entire industry Other people have their pretend magic formula that they somehow created, but we did all the work for years and years in order to create the perfect system that has helped us to earn a lot If you want to get big money and win every week, visit below offer for CORRECT SCORE: NEXT CORRECT SCORE MATCHES IS FOR: 06 02 2021 SATURDAY (100% CORRECT SCORE MATCHES) One Match Correct Score Full Time Result Odds about 150 00 Match is from strong source and no chance for lost! Match: – Tip: Correct Score (full time) Odd: 149 00 Receive 1 correct score bet Based on the exact score betting Bet on the exact score of a game Full Time results Average odds of 150 Two selection bet on either one with an average odd of 150 based on all past records Guaranteed odds available in your sportsbook If the odds are not available in your sportsbook we will provide you replacement with the next coming tip High return betting Single bet to achieve a high return with average odds 150 00 Correct Score Archives Correct Score Archives LAST CORRECT SCORE MATCH 03 02 2021 DATE MATCHES TIP ODD FT W/L 03 02 2021 Enppi – Pyramids CS 3:2 51 00 3:2 CLICK ON PRICTURE TO VISIT PROOF FROM OUR BET AND WHAT'S APP (03 02 2021 Wednesday Proofs) LAST CORRECT SCORE MATCH 30 01 2021 DATE MATCHES TIP ODD FT W/L 30 01 2021 Brentford – Wycombe CS 7:2 251 00 7:2 CLICK ON PRICTURE TO VISIT PROOF FROM OUR BET AND WHAT'S APP (30 01 2021 Saturday Proofs) LAST CORRECT SCORE MATCH 23 01 2021 DATE MATCHES TIP ODD FT W/L 23 01 2021 Wealdstone – Aldershot CS 3:4 51 00 3:4 CLICK ON PRICTURE TO VISIT PROOF FROM OUR BET AND WHAT'S APP (23 01 2021 Saturday Proofs) Daily Single Matches 100% Sure Daily Single Matches 100% Sure Payment methods we accept: western union,moneygram,neteller,skrill and bitcoin For more details how to subscribe, contact us: Daily Combined Fixed Matches Daily Combined Fixed Matches DATE MATCHES TIP ODD FT W/L 01 02 2021 Sporting – Benfica Santa Clara – Belenenses 1 1 3 10 2 25 1:0 2:0 02 02 2021 Wycombe – Birmingham Swindon – Wigan X 1 3 25 2 55 0:0 1:0 03 02 2021 – – 04 02 2021 – – 05 02 2021 – – 06 02 2021 – – 07 02 2021 – – 08 02 2021 – – 09 02 2021 – – 10 02 2021 – – 11 02 2021 – – 12 02 2021 – – 13 02 2021 – – 14 02 2021 – – 15 02 2021 – – 16 02 2021 – – 17 02 2021 – – 18 02 2021 – – 19 02 2021 – – 20 02 2021 – – 21 02 2021 – – 22 02 2021 – – 23 02 2021 – – 24 02 2021 – – 25 02 2021 – – 26 02 2021 – – 27 02 2021 – – 28 02 2021 – – 29 02 2021 – – 30 02 2021 – – 31 02 2021 – – Free Predictions Analyzed Matches Free Predictions Analyzed Matches HOW TO GET FIXED MATCHES? Next fixed matches is available for the next date 03 02 2021 WEDNESDAY (Jacob Johnson Site Fixed Matches) IMPORTANT: For all visitors, free soccer predictions is not a 100% sure matches, this is only 50% sure matches, if you want 100% sure fixed matches contact us! FREE SOCCER PREDICTIONS 03 02 2021 DATE MATCHES TIP ODD FT W/L 03 02 2021 RB Leipzig – Bochum 1 1 30 03 02 2021 Monaco – Nice 1 1 55 03 02 2021 Brentford – Bristol City 1 1 45 FREE SOCCER PREDICTIONS 01 02 2021 DATE MATCHES TIP ODD FT W/L 01 02 2021 Watford – QPR 2 4 75 1:2 01 02 2021 Anorthosis – Karmiotissa 1 1 30 2:1 01 02 2021 Betis – Osasuna 1 2 00 1:0 FREE SOCCER PREDICTIONS 31 01 2021 FREE SOCCER PREDICTIONS 30 01 2021 DATE MATCHES TIP ODD FT W/L 30 01 2021 D Zagreb – Sibenik BTTS 1 90 1:2 30 01 2021 Kawkab Marrakech – Wydad Fes BTTS 2 10 2:1 30 01 2021 Inter – Benevento 1 1 30 4:0 30 01 2021 FCSB – Poli Iasi 1 1 25 3:1 30 01 2021 Sampdoria – Juventus X2 1 20 0:2 FREE SOCCER PREDICTIONS 29 01 2021 DATE MATCHES TIP ODD FT W/L 29 01 2021 Hatta – Al Wahda BTTS 1 90 1:4 29 01 2021 Antwerp – WaaslandBeveren 1 1 50 3:2 29 01 2021 Stuttgart – Mainz BTTS 1 60 2:0 FREE SOCCER PREDICTIONS 28 01 2021 DATE MATCHES TIP ODD FT W/L 28 01 2021 Napoli – Spezia 1 1 40 4:2 28 01 2021 Ajax – Willem II 1 1 20 3:1 28 01 2021 Melipilla – Rangers BTTS 2 10 1:1 FREE SOCCER PREDICTIONS 27 01 2021 DATE MATCHES TIP ODD FT W/L 27 01 2021 Juventus – Spal 1 1 25 4:0 27 01 2021 Chelsea – Wolves 1X 1 25 0:0 27 01 2021 Celtic – Hamilton 1 1 20 2:0 FREE SOCCER PREDICTIONS 26 01 2021 FREE SOCCER PREDICTIONS 25 01 2021 DATE MATCHES TIP ODD FT W/L 25 01 2021 Wycombe – Tottenham 2 1 25 1:4 25 01 2021 Le Mans – Red Star 1 3 10 2:1 25 01 2021 Como – Renate 1 2 75 2:1 FREE SOCCER PREDICTIONS 24 01 2021 FREE SOCCER PREDICTIONS 23 01 2021 DATE MATCHES TIP ODD FT W/L 23 01 2021 Monaco – Marseille 1X 1 30 3:1 23 01 2021 Fiorentina – Crotone 1X 1 25 2:1 23 01 2021 PSV – Waalwijk 1 1 25 2:0 FREE SOCCER PREDICTIONS 22 01 2021 DATE MATCHES TIP ODD FT W/L 22 01 2021 Cambuur – Jong PSV 1 1 25 5:1 22 01 2021 Almere City – Dordrecht 1 1 25 3:0 22 01 2021 FCSB – FC Voluntari 1 1 25 2:1 FREE SOCCER PREDICTIONS 21 01 2021 DATE MATCHES TIP ODD FT W/L 21 01 2021 Valencia – Osasuna X 3 30 1:1 21 01 2021 Eibar – Atl Madrid 2 1 80 1:2 21 01 2021 Liverpool – Burnley 2 10 00 0:1 FREE SOCCER PREDICTIONS 20 01 2021 DATE MATCHES TIP ODD FT W/L 20 01 2021 Manchester City – Aston Villa 1 1 30 2:0 20 01 2021 Fulham – Manchester Utd 2 1 60 1:2 20 01 2021 Schalke – FC Koln 2 2 40 1:2 FREE SOCCER PREDICTIONS 19 01 2021 DATE MATCHES TIP ODD FT W/L 19 01 2021 West Ham – West Brom 1 1 60 2:1 19 01 2021 Leicester – Chelsea 1 2 75 2:0 19 01 2021 Mainz – Wolfsburg 2 1 75 0:2 FREE SOCCER PREDICTIONS 18 01 2021 DATE MATCHES TIP ODD FT W/L 18 01 2021 Arsenal – Newcastle 1 1 50 3:0 18 01 2021 Cagliari – AC Milan 2 1 60 0:2 18 01 2021 Morocco – Togo 1 1 30 1:0 FREE SOCCER PREDICTIONS 17 01 2021 DATE MATCHES TIP ODD FT W/L 17 01 2021 Sheffield – Tottenham 2 1 90 1:3 17 01 2021 Manchester City – Crystal Palace 1 1 20 4:0 17 01 2021 Lyon – Metz 2 8 50 0:1 FREE SOCCER PREDICTIONS 16 01 2021 surefixedmatch club/statareafixedmatchesodd30vs4 Statarea fixed matches odd 30 vs 4 surefixedmatch club/floridasportsbettingbill Florida sports betting bill surefixedmatch club/besttipsandpredictionstoday Best tips and predictions today surefixedmatch club/sportsbettingsiteswithlowdeposits Sports betting sites with low deposits surefixedmatch club/wojnbadraft Woj nba draft surefixedmatch club/nflharrisonpicks Nfl harrison picks surefixedmatch club/expertpicksncaatournament2020 Expert picks ncaa tournament 2020 surefixedmatch club/paidpicks1x2 Paidpicks1x2 surefixedmatch com Belgium Fixed Matches surefixedmatch club/week4nfloddsvegas Week 4 nfl odds vegas surefixedmatch club/soccer6v1p1 Soccer 6 v1 p1 surefixedmatch club/bestpositiontodraftinfantasyfootball Best position to draft in fantasy football surefixedmatch club/championtip1x2 Champion tip 1x2 surefixedmatch club/nflpicksagainstthespreadweek52020 Nfl picks against the spread week 5 2020 surefixedmatch club/collegeoddsshark College oddsshark surefixedmatch club/hbosportspicks Hbo sports picks surefixedmatch club/sportsgamblingfacts Sports gambling facts surefixedmatch club/predictionsforthursdaynightfootballgame Predictions for thursday night football game surefixedmatch club/bozinovskaatipbloger Bozinovskaa tip bloger surefixedmatch club/bestonlinesportsbettingnj Best online sports betting nj surefixedmatch club/classicsbrodds Classic sbr odds surefixedmatch club/statareaweekendcorrectscore Statarea weekend correct score surefixedmatch club/sportsbettinglinesandodds Sports betting lines and odds surefixedmatch club/fiveteamparlayodds Five team parlay odds surefixedmatch com fixed matches malmo surefixedmatch club/fantasyvaluepicks Fantasy value picks surefixedmatch club/2020nflweek2picks 2020 nfl week 2 picks surefixedmatch club/realbettingsoccerprediction Real Betting Soccer Prediction surefixedmatch club/mickeymoniak2020 Mickey moniak 2020 surefixedmatch club/totesportsignin Totesport sign in surefixedmatch club/betblogspot Bet blogspot surefixedmatch club/tennisodds Tennis odds surefixedmatch club/bestbettingblogs Best betting blogs surefixedmatch club/earlymorningfootballtips Early morning football tips surefixedmatch club/nflagainsttheodds Nfl against the odds surefixedmatch club/nflconfidencepoolpicksweek15 Nfl confidence pool picks week 15 forum hangarnet com br/member php?action=profile&uid=82600 fixedmatches 1x2 tips lchfforum se/member php?38802WonImmanty Fixed Uk Football Games forums bsdinsight com/resources/authors/wonenfots 69728/ FREE DAILY SOCCER TIPS forum shopheroesgame com/viewtopic php?f=4&t=230174 Football Fixed Matches Today forum grafol ru/memberlist php?mode=viewprofile&u=82754 Fixed Ticket 1X2 retroworld gr/index php?/user/19364woncrite/ Fixed Soccer Betting Picks zandroid com/engine/ajax/addcomments php Global Fixed Matches Tips abactoday com/members/wonveste/profile/ Iceland Fixed Matches hausforum ch/profile/46170wonordime/ fixedmatches 420 com xn34gday2frmnaav988bbn6hdcawt0e 100elearning com/space php?uid=55532 how to get correct score without paying serenityisles com/forum/memberlist php?mode=viewprofile&u=2168 Football Fixed Matches gangsterleague 000webhostapp com/forum/viewtopic php?f=3&t=417699 international fixed games bonsaiforums com/user/Wongaisee g fixed matches um disnaikid com/space php?uid=9042 Ireland Fixed Matches manymen men/?action=post;board=11 0 Genuine Betting Tips Today forum thatchedgroup com/memberlist php?mode=viewprofile&u=209723 Football Fixed Games forum xbadm ru/member php?u=87453 Get Sure Fixed Matches heroscapers com/community/member php?u=269919 Football Correct Fixed Matches xnoor418pqmhbo7kk01b phtourass com tw/discuz/space php?uid=193121 International Fixed Matches 1×2 forchess ru/member php?u=91940 Free Soccer Predictions shareae com/engine/ajax/addcomments php fixedmatches uk vietstamp net/forum/member php?u=159281 Get Real Fixed Matches home4all gromader org/member php?u=132615 fixedmatches 1x2 net foro viajarafrancia com/member92544 html?sid=df105de3b5e73775c9c67b445a44db5b hot fixed games radiorecord fm/user/WonPaype/ Football uk Sure Predictions forum vina ir/member php?action=profile&uid=66646 global real fixed matches 

04.06.2021, 07:36  #19510 (permalink)  

Free Porn Pictures and Best HD Sex Photos
Nude Sex Pics, Sexy Naked Women, Hot Girls Porn
Woodway men obcessed with porn free bj sex porn mexico porn channel vern correa porn adriana amante porn 48 Hot galleries, thousands new daily. Minocqua best quicktime porn hugh hefner porn videos nude porn aunty videos rapidshare pigtail virgins torrent teen porn shuf porn 1 Hot galleries, daily updated collections Jasonville great vintage porn porn scooby doo tane stone video porn best anime porn games cigars porn movies 2 Young Heaven  Naked Teens & Young Porn Pictures TGP Porn Sites lezbion free porn games flip porn amiee porn mouse furry porn 70 s porn free 2 Sexy photo galleries, daily updated pics Falls Village uk porn galleries free anthony gonzales porn okinawa streaming porn video smoking totally uncut gay porn got gay porn cum 3 Sexy teen photo galleries Free High Porn Quality Pics and Erotic Galleries For You black porn mr cheeks porn photos of prom college adult porn cute little teen porn pix nyc porn video eighth ave 4 Girls of Desire: All babes in one place, crazy, art http://teen.porn.diamond.danexxx.com/?mckayla young and old streaming porn atm machine porn dirty daughter short porn clips girl on girl poseing for porn earning sex porn videos 4 Sexy photo galleries, daily updated pics Lotsee moms hot porn mommy video porn amatuer anal porn videos very young teen interracial porn free porn videos indexed 7 Enjoy our scandal amateur galleries that looks incredibly dirty List of Top Porn Sites byron long porn star candy samples porn tubes mexican kiddie porn free sex porn hot naked porn syars gingerse2; 04.06.2021 07:44. : 
