LAX IMPACT! An Exploration into Advanced Analytics for Major League Lacrosse By R. Alan Eisenman April 2016 State of Lacrosse Statistics: As Major League Lacrosse enters its 11 th season on April 23 rd , 2016 the statistical revolution that has appeared in other sports, namely baseball and basketball, has not yet arrived for professional lacrosse. The league will add a 9 th team for the 2016 season and discussions for a 10 th are in the works 1 showing clear signs of league growth. Additionally, with the announcement of a league wide streaming deal with the Lax Sports Network 2 and national TV coverage (CBS Sports Network) for the MLL All-Star Game, Semifinal, and Championship games 3 exposure will be at an all-time high. As team revenues grow player salaries will inevitably rise which will necessitate a focus for teams on finding ways to properly evaluate their player assets. While traditional “counting” statistics can provide some insight into a player’s impact on the field, these statistics can be misleading and leave many unanswered questions. The introduction of advanced statistical analysis and analytics can provide an avenue for teams to measure the return a player provides to his franchise. Shortcoming of Current Statistics: As was the case in most other sports before their respective statistical revolutions, lacrosse statistics have not progressed far beyond “counting stats.” Counting stats are totals that ignore the context that surrounds them, think goals and assists. For example, if one looked at a team’s statistics page and saw that Player A had five goals this year, they might think it was a down year without any context to inform the statistic. However, when taken into context that he only played in two games this season the perception changes drastically. This brings about the notion of rate statistics that can help put context around counting statistics by bringing them to a common denominator. Additionally, current lacrosse statistics cannot explain the type of style that a team employs. The style of play may drastically affect the number of opportunities a player has to accumulate statistics. For example, some teams may be more aggressive on attack while others may play slower, more deliberate style. This obviously will have a large impact on the sheer volume of opportunities that players have to accumulate statistics. The most famous example of this problem solved is the introduction of per 1 MLL to Use June All-Star Game in Houston as Gauge for Expansion Interest. (2015, February 27). Retrieved from Sports Business Daily: http://www.sportsbusinessdaily.com/Daily/Issues/2015/02/27/Leagues-and-Governing-Bodies/MLL- Houston.aspx?hl=Major 2 Bulovas, M. (2016, April 22). Weekend Plans with MLL Commissioner David Gross: Starting the Season, Family Time. From Sports Business Daily: http://www.sportsbusinessdaily.com/Daily/Issues/2016/04/22/People-and-Pop-Culture/Weekend.aspx 3 Buzzer Beaters. (2016, March 30). Retrieved from Sports Business Daily: http://www.sportsbusinessdaily.com/Daily/Closing- Bell/2016/03/30/Beaters.aspx www.majorleaguelacrosse.com
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Transcript
LAX IMPACT!
An Exploration into Advanced Analytics for Major League
Lacrosse
By R. Alan Eisenman April 2016
State of Lacrosse Statistics: As Major League Lacrosse enters its 11th season on April 23rd, 2016 the statistical revolution that
has appeared in other sports, namely baseball and basketball, has not yet arrived for professional
lacrosse. The league will add a 9th team for the 2016 season and discussions for a 10th are in the works1
showing clear signs of league growth. Additionally, with the announcement of a league wide streaming
deal with the Lax Sports Network2 and national TV coverage (CBS Sports Network) for the MLL All-Star
Game, Semifinal, and Championship games3 exposure will be at an all-time high. As team revenues grow
player salaries will inevitably rise which will necessitate a focus for teams on finding ways to properly
evaluate their player assets. While traditional “counting” statistics can provide some insight into a
player’s impact on the field, these statistics can be misleading and leave many unanswered questions.
The introduction of advanced statistical analysis and analytics can provide an avenue for teams to
measure the return a player provides to his franchise.
Shortcoming of Current Statistics: As was the case in most other sports before their respective statistical revolutions, lacrosse
statistics have not progressed far beyond “counting stats.” Counting stats are totals that ignore the
context that surrounds them, think goals and assists. For example, if one looked at a team’s statistics
page and saw that Player A had five goals this year, they might think it was a down year without any
context to inform the statistic. However, when taken into context that he only played in two games this
season the perception changes drastically. This brings about the notion of rate statistics that can help
put context around counting statistics by bringing them to a common denominator.
Additionally, current lacrosse statistics cannot explain the type of style that a team employs. The
style of play may drastically affect the number of opportunities a player has to accumulate statistics. For
example, some teams may be more aggressive on attack while others may play slower, more deliberate
style. This obviously will have a large impact on the sheer volume of opportunities that players have to
accumulate statistics. The most famous example of this problem solved is the introduction of per
1 MLL to Use June All-Star Game in Houston as Gauge for Expansion Interest. (2015, February 27). Retrieved from Sports
Business Daily: http://www.sportsbusinessdaily.com/Daily/Issues/2015/02/27/Leagues-and-Governing-Bodies/MLL-Houston.aspx?hl=Major 2 Bulovas, M. (2016, April 22). Weekend Plans with MLL Commissioner David Gross: Starting the Season, Family Time. From
Sports Business Daily: http://www.sportsbusinessdaily.com/Daily/Issues/2016/04/22/People-and-Pop-Culture/Weekend.aspx 3 Buzzer Beaters. (2016, March 30). Retrieved from Sports Business Daily: http://www.sportsbusinessdaily.com/Daily/Closing-
Bell/2016/03/30/Beaters.aspx
www.majorleaguelacrosse.com
possession4 metrics in basketball. This allows teams’ statistics to be evaluated on a consistent basis that
takes into account the style of basketball they play. Once the statistics are on a comparable basis this
allows for the next step of analysis: the efficiency at which teams accumulate statistics and ultimately
perform on the field.
While there are some blogs and articles written on the subject of lacrosse analytics (those that
are mostly focus on college lacrosse), there does not seem to be any examples of application in
professional lacrosse. Thus, smart teams will be well served to invest in advanced statistical analysis in
order to more effectively and efficiently evaluate team and player performance. This will allow teams to
more appropriately utilize time and monetary resources in search of success on the field.
Advanced Analytics Application: LAX IMPACT! In the following section I will propose one solution to this problem: a new advanced statistic
called LAX IMPACT!. This statistic is essentially a points per possession metric that will allow teams to
better evaluate offensive players’ impact on the game.
Similar to basketball, we must first develop a team possession metric to quantify the team’s
style of play. Basketball possession is based on the various ways in which teams can end offensive
possessions. The basketball formula is 0.96*(Field Goals Attempted – Off Rebounds + Turnovers + (.44 *
Free Throws Attempted)). This should be intuitive to those that know basketball as every offensive
possession ends in a shot attempt, a free throw attempt or a turnover. Due to the lack of publicly
available lacrosse statistics some creativity is needed. Here are the factors I considered:
Faceoff Wins (FOW) - Unlike basketball, after a goal in lacrosse the ball does not automatically
go to the other team, but rather a faceoff is contested. Teams that win more faceoffs than they
lose clearly possess the ball longer.
Total Shots – Shots may be representative of the amount of time an offense was in the offensive
zone; however, there is a rule in lacrosse that errant shots that go out of bounds are returned to
the team closes to the out of bounds line instead of automatically going to the opposing team.
Though it could be assumed that most shots return to the offense (most teams “back up” shots
for this purpose), data on the actual number of times the ball goes to the opposing team is not
publicly available.
Shots on Goal (SOG) – SOG may be better than Shots because it has a more definitive result:
goal, save, or rebound. However, there is a lack of publicly available data on the number of
shots that are actually recovered by the offense (i.e. “offensive rebounds).
Ground Balls (GB) - Ground balls are credited when one team gains possession of a ball that is
currently not in either team’s possession. This may be a good metric as is representative of
“hustle” and is a clear indication of possession.
Defensive Zone Clears – This would be a good metric to determine the number of times the
defense stops the opposing team’s offense and goes on the attack. However, publicly available