Monday, May 20, 2013
2012 MLS Designated Player ROI Rankings
Using Dave Laidig's Weighted Castrol Index model and salaries released by the player's union we can try and quantify how many points each player is responsible for creating for their team and how much this contribution cost the team. Below are the ROI rankings of MLS' one percent, the $500k salary club.
Wednesday, May 15, 2013
Top 20 MLS Best Values of 2012
Using Dave Laidig's Weighted Castrol Index model and salaries released by the player's union we can try and quantify how many points each player is responsible for creating for their team and how much this contribution cost the team. Below are the top MLS best values of 2012 (Salary Per Points Contributed)
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Friday, May 10, 2013
EPL vs. MLS: Style Comparison
I have previously compared the EPL and MLS, with the slant being quite pro-MLS. The argument was that there was not much between the leagues in terms of turnover, goal, and shot rate. However, this analysis tries to understand what is happening between a possession's endpoints; what is the passing rate/style of each league? Many people have stated that the "speed of play" in the EPL is on another level. This analysis would lend support to that statement. On average, 2011-2012 EPL teams (only year for which data could be obtained) attempted 3.73 passes per possession whereas 2013 MLS teams attempt 2.81 passes per possession, implying that the average EPL team's speed of play is 32.7% faster than the average 2013 MLS team. Of course, MLS' quality of play is likely to improve over the rest of the season, but the differences between the leagues and individual teams are interesting.
Tuesday, April 30, 2013
Friday, April 26, 2013
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Monday, March 4, 2013
MLS Tempo-Free Soccer (TFS) Rankings Methodology
Overview
I believe that framing things on a per possession basis in soccer is an effective way to evaluate teams and players. What TFS attempts to answer is: When you have possession, what do you do with it? Do you score? Do you turn it over? What do your opponents do when they have possession?
Borrowing heavily from tempo-free statistics pioneers Ken Pomeroy and Dean Oliver, who have done excellent work with basketball, all statistics are framed on a per possession basis. Luckily for Pomeroy and Oliver, it is relatively easy to determine what constitutes a possession in basketball. Soccer is much more tricky.
Possession
Would-be soccer statisticians have long been flummoxed by the lack of available statistics. In recent years however, companies such as OPTA have taken on the large task of compiling a more comprehensive look at a game. Indeed, it is OPTA's game reports for MLS that form the basis of these rankings.
My starting point is the "Tackled, Possession Lost" (TPL) metric. TPL is assessed for any errant pass, interception, failed dribble, etc. A typical total for a team is between 120 and 160 a game.
The second component in determining the number of possessions is the "Clearances" (C) metric. According to OPTA, all Clearances are TPL, but not all clearances are changes in possession. For example, if a defender clears the ball out of bounds, then the team that originally lost the ball never really lost possession. Therefore, the number of Clearances, less Clearances where the defense maintains possession, is subtracted out of the TPL total. This subtotal is the number of Turnovers Committed.
Since it is Tempo-Free Soccer's opinion that all possessions end in either a Turnover or an "Attempt on Goal" (AOG), the equation for Possessions can be written as:
Possessions = TPL - C + AOG
One problem with the Tempo-Free soccer analysis is that very rarely will the number of possessions each team has equal each other. That said, the difference rarely exceeds 10 and is often closer to 5. Over the course of the season the disparity appears to sort itself out. The reasons for the consistent disparities are instances of "double possessions": a player may lose possession of the ball but it is adjudged to have gone off the other team, an attempt on goal is rebounded and another attempt is made, etc. It is my belief that even if a discerning eye were to go back over the entire 90 minutes, a complete reconciliation would be nearly impossible.
Expected Goals
EG = (AOG X Conversion %) + (SOG X Conversion %)
2
EG is a general metric taking the two major components of scoring goals (AOG, SOG) into consideration. Its aim is to roughly predict a team's expected goals scored per possession and goals allowed per possession. Conversion rates (Goals/AOG and Goals/SOG) are highly variable from team to team in a single season but generally consistent over time. Therefore, creating more opportunities than your opponent on a consistent basis is the primary driver to having a positive goal differential, winning soccer games, and accruing more points over the course of a season.
EG is derived by calculating the league average conversion rates for AOG and SOG and then assuming each team converts at the league average. At this point, I am weighting AOG and SOG equally, but this may be subject to change.
Luck
Luck = Points Per Match - Expected Points Per Match
For some reason, a long time ago someone decided that a win should count for three points, a draw for one, and a loss as zero. Margin of victory matters not one bit (except in case of tiebreaker). From a statistical point of view, the win/loss/draw system is silly human artifice. Some teams benefit from it (winning close games) and others do not.
Expected Points Per Match are calculated by creating a best fit line where Goal Differential is the X variable and Points Per Match are the Y variable.
I believe that framing things on a per possession basis in soccer is an effective way to evaluate teams and players. What TFS attempts to answer is: When you have possession, what do you do with it? Do you score? Do you turn it over? What do your opponents do when they have possession?
Borrowing heavily from tempo-free statistics pioneers Ken Pomeroy and Dean Oliver, who have done excellent work with basketball, all statistics are framed on a per possession basis. Luckily for Pomeroy and Oliver, it is relatively easy to determine what constitutes a possession in basketball. Soccer is much more tricky.
Possession
Would-be soccer statisticians have long been flummoxed by the lack of available statistics. In recent years however, companies such as OPTA have taken on the large task of compiling a more comprehensive look at a game. Indeed, it is OPTA's game reports for MLS that form the basis of these rankings.
My starting point is the "Tackled, Possession Lost" (TPL) metric. TPL is assessed for any errant pass, interception, failed dribble, etc. A typical total for a team is between 120 and 160 a game.
The second component in determining the number of possessions is the "Clearances" (C) metric. According to OPTA, all Clearances are TPL, but not all clearances are changes in possession. For example, if a defender clears the ball out of bounds, then the team that originally lost the ball never really lost possession. Therefore, the number of Clearances, less Clearances where the defense maintains possession, is subtracted out of the TPL total. This subtotal is the number of Turnovers Committed.
Since it is Tempo-Free Soccer's opinion that all possessions end in either a Turnover or an "Attempt on Goal" (AOG), the equation for Possessions can be written as:
Possessions = TPL - C + AOG
One problem with the Tempo-Free soccer analysis is that very rarely will the number of possessions each team has equal each other. That said, the difference rarely exceeds 10 and is often closer to 5. Over the course of the season the disparity appears to sort itself out. The reasons for the consistent disparities are instances of "double possessions": a player may lose possession of the ball but it is adjudged to have gone off the other team, an attempt on goal is rebounded and another attempt is made, etc. It is my belief that even if a discerning eye were to go back over the entire 90 minutes, a complete reconciliation would be nearly impossible.
Expected Goals
EG = (AOG X Conversion %) + (SOG X Conversion %)
2
EG is a general metric taking the two major components of scoring goals (AOG, SOG) into consideration. Its aim is to roughly predict a team's expected goals scored per possession and goals allowed per possession. Conversion rates (Goals/AOG and Goals/SOG) are highly variable from team to team in a single season but generally consistent over time. Therefore, creating more opportunities than your opponent on a consistent basis is the primary driver to having a positive goal differential, winning soccer games, and accruing more points over the course of a season.
EG is derived by calculating the league average conversion rates for AOG and SOG and then assuming each team converts at the league average. At this point, I am weighting AOG and SOG equally, but this may be subject to change.
Luck
Luck = Points Per Match - Expected Points Per Match
For some reason, a long time ago someone decided that a win should count for three points, a draw for one, and a loss as zero. Margin of victory matters not one bit (except in case of tiebreaker). From a statistical point of view, the win/loss/draw system is silly human artifice. Some teams benefit from it (winning close games) and others do not.
Expected Points Per Match are calculated by creating a best fit line where Goal Differential is the X variable and Points Per Match are the Y variable.
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