Soccer Orb’s resident statistician, Steve, shared an interesting bit of research with us a couple of days ago (See Premiership Ratings: Biggest Bang for the Buck from June 6). I’m going to swim into some dangerous waters and interpret his findings with my quasi-layperson’s eyes.
His model was simple, but very revealing. Using EPL wage data from the 2006-7 season, he built a model that, in words, looks something like this:
For each of the twenty clubs, the total number of points earned in Premier League matches is a function of the total (player) wages paid by the club for that season.
In other words, how much of any given club’s success can be explained by the quality of its roster? “Quality” cannot be perfectly measured by a number, but a player’s salary is the proxy variable that Steve used, mostly because wages were a major factor in the recent Deloitte report of the financial condition of European football.
Steve’s results were quite strong, given that he was trying to learn how much one factor–wages–explained team success. Also, he was forced to use a small sample–twenty observations, confined to just one league. It would be great to have this data over a period of five or so years, and to have the numbers not just for the EPL but also for La Liga, Serie A, and the Bundesliga. Steve, the cheapskate, wasn’t willing to cough up £600 for this year’s full report.
According to his model, about two-thirds of the variation in a team’s points could be explained by the variation in the wages that it paid to its players. It is important to understand that, although wages are a powerful predictor of team success, they’re not the only predictor. One-third of the variation in team points is due to other factors besides wages, factors that were not included in this model. Why weren’t they included? Because Steve wanted to isolate the effect of wages, since they were given a lot of attention by the Deloitte analysts.
If you look at his graph (again, it’s in the post before this one), you’ll see the strong positive relationship between salaries and Premiership points. To be more precise, each £1 million pound increase in salaries changes the season’s point total by .46.
Steve noted that Manchester United’s point total for the season was actually seventeen points higher than the simple wage model predicted. I immediately jumped on this as evidence of the “Sir Alex Ferguson” effect. His long experience and eye for youthful, underpriced talent contributed to his ability to get more points out of his players, regardless of their quality as proxied by salaries.
What about Chelsea? If you look at Steve’s graph, you’ll see that its data point lies below the regression line. (Insert gloating smirk-face here). That is, given the considerable amount by which the Chelsea roster lightened Abramovich’s checkbook, the team finished the season with fewer points than the model predicted. Why? Well, you tell me. What variables would help to improve the explanatory power of the model? No matter what you come up with, it’s clear that Jose Mourinho & Co. didn’t manage their costly resources very well.
Models like this are a good starting point in helping to isolate the factors associated with a club’s success. But you have to remember that the analyst is always constrained by the data that are available. It would have been great to have had several years’ worth of data so that a time-series analysis could be performed. Then you “build a more dynamic model,” according to Steve. For example, you could measure the extent to which last years’ success influences the current year’s salaries, (because a good year means more money from shirt sales, or whatever), which as we’ve seen has a big impact on the current year’s success.
Many thanks to Steve for putting his statistical expertise to work on salary-performance relationship. (I won’t thank him too much because I know that he’d gladly spend all his waking hours doing sports stats analysis instead of that options volatility stuff).
A final word: fortunately, no model can completely explain success on the pitch. As someone who followed baseball and (American) football first, to my eyes soccer is inherently difficult to quantify. I hope it stays that way. Soccer’s beauty would be diminished if all of its mysteries were revealed through regression analysis.
And now it’s time for Germany-Poland. Then, a few hours on my knees praying that Argentina beats us by no more than three or four goals…
