# Most Valuable Goal Scorer: It’s Not Who You Think

by Russ Goodman, Ph.D.

I lead a double life — mathematics professor by day, women’s soccer coach by night. It was only a matter of time before I found a way to combine my two passions under sports analytics.

Sports analytics is all about studying data to gain an advantage on the field of play. As goalkeepers coach for Central College, I look for any advantage to help our team allow as few goals as possible. A natural question is, “Who are the top meaningful goal-scorers for our next opponent?”

The answer comes from understanding that there is a quantifiable difference between the value brought by a player who scores only one goal in a season, in “garbage time” in a blowout win or loss, versus a player whose only goal for the season was a 90th-minute, game-winning goal. The 90th-minute, game-winning goal is more valuable because it more significantly impacts a team’s likelihood to win a game, and to earn points in the standings.

As a result, I aimed to find a way to quantify the value of a goal being scored. This is accomplished via my Expected Points Added (ExPA) metric, and the joy of my mathematics and soccer worlds colliding [1] is complete.

Who is the Most Dangerous Player?

In most organized soccer leagues, including our Iowa Intercollegiate Athletic Conference (IIAC), standings are determined by total number of match points earned: three points for a win, one point for a tie, zero points for a loss. Using ExPA, I quantify the value of a goal through a function that takes into account the following variables: when in a match the goal was scored, whether the match was home or away, and the resulting state of the game (a term for the resulting score differential after the goal).

Using ideas from probability and the past five years’ worth of IIAC women’s soccer game data [3], I created ExPA to indicate how many additional points a player’s goal would likely contribute to their team’s point total in the standings. The outputs for ExPA are decimals from 0.000 to 2.000.

For example:
A 90th-minute goal, changing the game’s state from 0 to +1, gives a maximal ExPA of 2.000, since the team went from a likely tie (one standings point) to a certain win (three standings points). This player adds two points to her team’s place in the standings.
An 80th-minute away goal, changing the game’s state from -2 to -1, gives an ExPA of 0.240, since the team is still losing away from home, although the goal offers a glimmer of hope to potentially tie (or maybe win) the game, salvaging (at least) one point.

I use ExPA to quantify the value of each of an IIAC goal-scorer’s in-conference goals, and thus identify the most meaningful in-conference goal-scorers. In my analysis of top 2015 IIAC goal-scorers, I found the leaderboard was quite debatable. At first glance, Katie Truesdale, a prolific and respected goal-scorer, ran away from the league in goals scored, while one of her teammates, Maddie Avery, was far behind with only three goals.

But when I ranked the league’s goal-scorers by ExPA, I read a different story.

While Truesdale scored the largest quantity of conference goals, she scored less meaningful goals than nine other conference players! Interestingly, Avery’s three goals cumulated to more value than Truesdale’s seven goals.

How does this information inform our defensive tactics regarding Truesdale and Avery? We always knew Truesdale was a prolific scoring threat, but we now know Avery is a player who scores meaningful goals, so we will aim to divide our defensive attention to those two threats as we deem appropriate. An analysis with ExPA merely informs our understanding of who might be the most dangerous player(s) on the field.

This is how sports analytics works. We study data to gain a more nuanced understanding of a sport, then use that understanding to make informed decisions. Is greater success on the field guaranteed? No. But if sports analytics helps coaches sleep more easily at night, able to defend their informed decisions, then the passionate analysts like me have done their job well.

Want to learn more about sports analytics? The Midwest Sports Analytics Meeting is on campus at Central College Nov. 19. Get details at https://tinyurl.com/MWSprtAnalytics16.

Russ Goodman is associate professor of mathematics and assistant women’s soccer coach at Central College in Pella, Iowa.

[1] Worlds colliding can sometimes be a bad thing too, just ask George Costanza: https://youtu.be/uPG3YMcSvzo

At Central College, curiosity is encouraged and exploration expected. This post is intended to stimulate vigorous, open inquiry, and opinions expressed belong to the author.