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04.06.2021, 00:20   #19501 (permalink)
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04.06.2021, 00:33   #19502 (permalink)
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04.06.2021, 00:46   #19503 (permalink)
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04.06.2021, 00:51   #19504 (permalink)
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04.06.2021, 01:45   #19505 (permalink)
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 Statistical association football predictions

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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 2016-17 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 Log-Likelihood: -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 2016-17 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 Log-Likelihood: -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 2016-17 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 Log-Likelihood: -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 2016-17 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 Log-Likelihood: -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 2016-17 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 Log-Likelihood: -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




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Woncreria   
04.06.2021, 03:58   #19506 (permalink)
: 82, : 1 : 82, : 1 : 82, : 1
: 99% : 99% : 99%
 
  Woncreria
 
: 21
.
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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
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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 half-points - 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?”
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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 10-40
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 10-40
Adding your Reading Test Score and Writing and Language Test Score becomes your Reading and Writing Test Score (which ranges from 20-80) This number is multiplied by 10 to get your Evidence-Based Reading and Writing Section Score (between 200-800)
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 200-800)
This means your total SAT® score can range from 400-1600
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 200-800 for the two sections (Evidence-Based Reading and Writing and Math), to give you a total SAT® score between 400-1600
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 11-12, 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 Evidence-Based 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 Evidence-Based 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 Evidence-Based 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), cross-test 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 up-to-date 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® Evidence-Based 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 full-length practice tests


MCAT Score Conversion Calculator | AAMC Practice Exam
The AAMC made this decision due to the COVID-19 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 full-length practice exam you plan to take Below are the questions you can skip to simulate the shortened exam


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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
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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 Match-fixing , 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 in-depth 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!
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NEXT MATCHES AVAILABLE ON
06 02 2021 SATURDAY (100% SURE FIXED MATCHES)
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Match: –
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Odd: 29 00
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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 Al-Najma – Al-Muharraq 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)
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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)
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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
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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 – Waasland-Beveren 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




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Woncreria   
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