#xga — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #xga, aggregated by home.social.
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However, this comes with plenty of caveats and blind spots.
xG assumes that shots are independent Bernoulli trials.
In reality, shots within a match are correlated.
A team that's behind on the scoreboard tends to generate more chances, but often of lower quality.
The model also doesn't account for how good (or bad) the goalkeeper is. Nor does it capture the pressure or motivation players feel when taking a shot.
It also fails to capture the tactical context of a match.
In the end, there are moments when it's like modeling market correlations with a Pearson correlation matrix during a financial panic. The dependence structure changes. You need copulas!
Even with its limitations, though, xG remains one of the best metrics we have for characterizing a football match.
The scoreboard tells you who won.
xG tells you who was more likely to have won based on the chances they created.
#ExpectedGoals #xG #FootballAnalytics #SoccerAnalytics #DataScience #SportsAnalytics #Probability #Statistics #MachineLearning #LogisticRegression #GradientBoosting #Bernoulli #ExpectedValue #Football #Soccer #WorldCup #FIFA #TacticalAnalysis #PerformanceAnalysis #SportsData #DataVisualization #Analytics #QuantitativeAnalysis #Mathematics #PredictiveModeling #FootballStats #xGA #Mbappe #Spain #Argentina
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However, this comes with plenty of caveats and blind spots.
xG assumes that shots are independent Bernoulli trials.
In reality, shots within a match are correlated.
A team that's behind on the scoreboard tends to generate more chances, but often of lower quality.
The model also doesn't account for how good (or bad) the goalkeeper is. Nor does it capture the pressure or motivation players feel when taking a shot.
It also fails to capture the tactical context of a match.
In the end, there are moments when it's like modeling market correlations with a Pearson correlation matrix during a financial panic. The dependence structure changes. You need copulas!
Even with its limitations, though, xG remains one of the best metrics we have for characterizing a football match.
The scoreboard tells you who won.
xG tells you who was more likely to have won based on the chances they created.
#ExpectedGoals #xG #FootballAnalytics #SoccerAnalytics #DataScience #SportsAnalytics #Probability #Statistics #MachineLearning #LogisticRegression #GradientBoosting #Bernoulli #ExpectedValue #Football #Soccer #WorldCup #FIFA #TacticalAnalysis #PerformanceAnalysis #SportsData #DataVisualization #Analytics #QuantitativeAnalysis #Mathematics #PredictiveModeling #FootballStats #xGA #Mbappe #Spain #Argentina
-
However, this comes with plenty of caveats and blind spots.
xG assumes that shots are independent Bernoulli trials.
In reality, shots within a match are correlated.
A team that's behind on the scoreboard tends to generate more chances, but often of lower quality.
The model also doesn't account for how good (or bad) the goalkeeper is. Nor does it capture the pressure or motivation players feel when taking a shot.
It also fails to capture the tactical context of a match.
In the end, there are moments when it's like modeling market correlations with a Pearson correlation matrix during a financial panic. The dependence structure changes. You need copulas!
Even with its limitations, though, xG remains one of the best metrics we have for characterizing a football match.
The scoreboard tells you who won.
xG tells you who was more likely to have won based on the chances they created.
#ExpectedGoals #xG #FootballAnalytics #SoccerAnalytics #DataScience #SportsAnalytics #Probability #Statistics #MachineLearning #LogisticRegression #GradientBoosting #Bernoulli #ExpectedValue #Football #Soccer #WorldCup #FIFA #TacticalAnalysis #PerformanceAnalysis #SportsData #DataVisualization #Analytics #QuantitativeAnalysis #Mathematics #PredictiveModeling #FootballStats #xGA #Mbappe #Spain #Argentina
-
However, this comes with plenty of caveats and blind spots.
xG assumes that shots are independent Bernoulli trials.
In reality, shots within a match are correlated.
A team that's behind on the scoreboard tends to generate more chances, but often of lower quality.
The model also doesn't account for how good (or bad) the goalkeeper is. Nor does it capture the pressure or motivation players feel when taking a shot.
It also fails to capture the tactical context of a match.
In the end, there are moments when it's like modeling market correlations with a Pearson correlation matrix during a financial panic. The dependence structure changes. You need copulas!
Even with its limitations, though, xG remains one of the best metrics we have for characterizing a football match.
The scoreboard tells you who won.
xG tells you who was more likely to have won based on the chances they created.
#ExpectedGoals #xG #FootballAnalytics #SoccerAnalytics #DataScience #SportsAnalytics #Probability #Statistics #MachineLearning #LogisticRegression #GradientBoosting #Bernoulli #ExpectedValue #Football #Soccer #WorldCup #FIFA #TacticalAnalysis #PerformanceAnalysis #SportsData #DataVisualization #Analytics #QuantitativeAnalysis #Mathematics #PredictiveModeling #FootballStats #xGA #Mbappe #Spain #Argentina