|Analysis of Division I Men’s Lacrosse Statistics|
|By Michael Mauboussin|
Summary and Next Steps
Because each team has 10 players who interact, lacrosse is not as easy a game to analyze as some other sports, including baseball and basketball. But because lacrosse has discrete possessions, it is possible to derive some statistical measures that can provide substantial insight into what matters on the field.
Here are some specific recommendations for next steps:
• Use more statistics in player evaluation. Each offensive player should be evaluated based on metrics, including shots per game and shooting percentage. Coaches should also analyze what percentage of a player’s shots are saved and hence are effectively turnovers. Offensive players should also be assessed based on turnovers net of caused turnovers. Consider the statistics for these two players for the 2011 season: 6
Player B obviously has better scoring stats, with more goals and assists. But player B had to average 9.9 shots per game in order to score 2.5 goals per game, while player A averaged just 4.4 shots per game to score 1.9 goals. So player A is substantially more efficient.
How these players contributed to possessions is another key issue. To do this, you might look at:
For player A, the net possession contribution is +5 (44 – 24 – 15 = 5). For player B, the net possession contribution is –54 (34 – 35 – 53 = –54). If you assume each possession is worth about 0.25 goals, player B “gave away” over 13 goals, while Player A added another goal.
For defensive players, the assessment is trickier. Some metrics, like shots taken and shooting percentage by opponents, is straightforward. Ground balls are also clear and can be assigned a numerical value. What’s more difficult is the contribution to team defense, proper slides, for example. Caused turnovers can be good (player takes away the ball but remains under control) or bad (player takes high a high-risk strategy).
• Do more detailed shot analysis. Teams should not only track shots but should also assess them based on where they originate on the field as well as their location on the goal. Players should be assessed for their tendencies (e.g., consistently shot high to high) as well as their mix between goals, saves, and wide shots. Players should learn where they are strong shooters and where their probabilities of success drop.
Teams should also assess the shooting percentage following a pass as well as their assist-to-goal ratio. If that ratio is substantially below or above average, coaches should figure out why and whether the passing game is adding or subtracting to offensive efficiency.
• Possession analysis. The importance of possessions is self-evident. Teams can plot their time of possessions and see if patterns emerge. Is the team particularly effective in transition (i.e., short possessions)? Do long possessions yield a higher percentage of goals? Is there an optimal possession length?
• Using statistics to recruit. Lacrosse is quickly becoming a national and even an international, sport. This has made recruiting both exciting and challenging. It is likely that better gathering and analysis of statistics at the high school level will lead to better assessments of a player’s potential in college. For example, progressive teams in professional hockey, basketball, and baseball have developed guidelines for translating performance at a lower level (be it high school, college, or a foreign league) into performance in their top league. For U.S. and Canadian players who participate in well-established conferences (which includes much of the Northeast corridor and Canada), college performance will correlate with high school performance.
Summary of Correlations
1 David J. Berri and Martin B. Schmidt, Stumbling on Wins: Two Economists Expose the Pitfalls on the Road to Victory in Professional Sports (Upper Saddle River, NJ: FT Press, 2010); Tobias J. Moskowitz and L. Jon Wertheim, Scorecasting: The Hidden Influences Behind How Sports and Played and Games Are Won (New York: Crown Archetype, 2011).
2 Matt DaSilva, “Great X-Pectations,” Lacrosse Magazine, October 2009, 11.
3 VMI threw this off. In 2011, the team had tremendous ground ball and face-off statistics but a tremendously porous defense. Excluding VMI, the teams won two-thirds of the games.
5 For the best data on Canadians in the NCAA, see JP Donville’s work at http://www.ailacrosse.net/news_article/show/78805?referrer_id=187104.
6 Player A is Steele Stanwick (Virginia), and Player B is Rob Pannell (Cornell).
7 Frank E. Kuzmits and Arthur J. Adams, “The NFL Combine: Does It Predict Performance in the National Football League?” The Journal of Strength and Conditioning Research, Vol. 22, No. 6, November 2008, 1721-1727.
Endnotes can be found at the bottom of Part 5. Appendix A appears in Part 6, and Appendices B and C are presented in Part 7.
Michael Mauboussin works an as investment strategist. He is a lacrosse fan, youth coach, and retired player. For more, see www.michaelmauboussin.com.
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