1. 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
2. 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
data does not exist on the subject.
4
Possessions = .96*(Field Goals Attempted – Off Rebounds + Turnovers + (.44 * Free Throws Attempted))
3. After considering each factor I decided to calculate team possessions by adding team faceoff
wins and ground balls. This should serve as a good proxy for possession if you assume: every shot off
goal is backed up; every shot on goal is scored, a save, or credited with a ground ball; and either time
the previous two don’t occur a ground ball is credited. Though this isn’t a perfect metric, it should
adequately approximate possession.
Next we must find a way to associate possessions to individual players. The easiest way to
approximate this would be to determine the team minutes the player actually played: minutes played /
(number of players x length of game). However, minutes played is not a publically available statistic at
this time. Therefore, I decided to use a proxy of SOG plus GB as a percentage of team SOG and GB. This
should serve to approximate the player’s relative contribution to offensive possessions. SOG is more
appropriate here than shots, as shots may be misleading because a shot could intentionally be off target
as part of team strategy.
Finally, we must determine the appropriate counting statistic to serve as the numerator in LAX
IMPACT!. While goals are an obvious choice there are some players that are not intended to be shooters
but rather distributors whose role cannot be understated. Therefore, I chose to use points which is goals
plus assists. This serves as a good metric to represent the objective outcome that a team is seeking:
goals in the back of the net.
We arrive at the following formula for LAX IMPACT! that will allow teams to better evaluate an
offensive player’s contribution to team success.
𝑃𝑜𝑖𝑛𝑡𝑠
(
(𝑃𝑙𝑎𝑦𝑒𝑟 𝑆𝑂𝐺 + 𝑃𝑙𝑎𝑦𝑒𝑟 𝐺𝐵)
(𝑇𝑒𝑎𝑚 𝑆𝑂𝐺 + 𝑇𝑒𝑎𝑚 𝐺𝐵)
) ∗ (𝑇𝑒𝑎𝑚 𝐹𝑂𝑊 + 𝑇𝑒𝑎𝑚 𝐺𝐵)
Case Study: 2015 MLL Data
The above discussed concepts were applied to publicly available 2015 Major League Lacrosse
Data5
to test its validity and evaluate its results. The player set used included the following
requirements: only attackmen and midfielders (no
defensemen or faceoff specialist) and a minimum of 10 GB +
SOG. Thus, there were only 114 players that qualified for the
rating.
Team Possession:
The average number of possessions for the league
was 47.32 with a max of 56.07 (New York) and a min of
36.79 (Chesapeake). While there was variance in the ground
ball distribution, the major driver for the difference in
possessions was New York and Chesapeake’s respective
5
All data gathered from www.pointstreak.com/lacrosse
Figure 1 – Average Team Possessions per Game
4. 69.1% and 34.7% faceoff win rate. Because New York controlled possession so much more than other
teams, it is no surprise that they had many more opportunities for scoring. This is highlighted in Figure 1
above where the color scale shows the number of shots on goal per team and the bar height represents
average possession. This clearly shows that teams with higher possession typically are able to get off
more SOG. However, as discussed before, the sheer volume of SOG on goal is not as relevant as the
efficiency of point scoring controlling for number of possessions.
LAX IMPACT! Leaders:
The top rated player in terms of LAX IMPACT! for the 2015 season was Steele Stanwick of the
Ohio Machine with a LAX IMPACT! rating of 1.59 as compared to a league average of 0.58. Stanwick’s
impact rating is almost 20% higher than the 2nd
place finisher John Glesener of the Boston Cannons
(1.33). Further investigation shows that Stanwick was extremely efficient in scoring 44 points on only 30
SOG and 7 GBs. This highlights an important takeaway of the LAX IMPACT! rating in that raw points
volume does not ensure a high LAX IMPACT! rating. In fact of the top ten players in terms of total points
only three finished top ten in LAX IMPACT!. For example, Rob Pannell (New York) was the league points
leader, but finished 42nd
in LAX IMPACT!.
Figure 2 below shows the top 20 rated players in terms of LAX IMPACT! colored again by SOG.
This reinforces the notion that LAX IMPACT! rewards players that make the most of their opportunities,
not the players that took the most shots.
Figure 2 – 2015 LAX IMPACT! Leaders
5. LAX IMPACT! Leaders by Team Position Group:
The below tree map (Figure 3) is built using the 75th
percentile of each team’s position group
(Attack and Midfield). The 75th
percentile gives a good approximation for how the top players on each
team perform relative to their counterparts on the other teams. Interestingly, the highest rated position
group was the Chesapeake Attack with a LAX IMPACT rating of 0.89. This may seem unexpected because
Chesapeake actually scored the 2nd
least number of goals in the 2015 season. However, because their
team possessions were the lowest in the league they actually were much more efficient in those
possessions than other teams. Similarly, the league’s best offense in terms of total goals, New York, had
a LAX IMPACT! rating of 0.67 attack, lowest in the league. This was driven by the number of possessions
used and indicates that they actually were not as efficient in those possessions as other teams.
Figure 3 – 2015 75
th
Percentile Position Group by Team
Drawbacks
While the above analysis certainly does provide valuable insight for teams there may still be
room for improvement. As was highlighted in the above discussion on methodology, without reliable
player minutes played data a proxy for game involvement must be used. The LAX IMPACT! rating tends
to underrate players that accumulate a lot of ground balls as this drives up their % of possessions used.
With minutes played data or a better proxy for game involvement the player possession % can be
improved. Additionally, LAX IMPACT! equally weights 1 point goals, 2 point goals, and assists. Further
study could be done to reassess the weights of these events to more accurately represent impact on the
game.
Additional data in a number of areas would improve the performance of the LAX IMPACT!
rating. Data on defensive zone clears would allow for a more accurate reflection of team possessions.
Turnover data on teams and players too would help solidify the possession metric. Finally, data on
“offensive rebounds” after SOG might help provide additional insight.
6. Takeaways
As Major League Lacrosse continues to grow and expand player salaries will inevitably rise. As
salaries rise teams must find a better way to evaluate their assets in order to field the best team they
can afford. The current state of lacrosse statistics is very basic and does not take into account context
very effectively. An introduction of advanced statistical analysis and analytics will help teams get a
better sense of player value. The proposed metric LAX IMPACT! is an implementable solution to this
problem. Using existing publicly data teams can use LAX IMPACT! to assess the contribution offensive
players make on the game. By taking into account team possessions both team and player statistics can
be better compared by accounting for a team’s style of play and tempo. LAX IMPACT! is just one
example of advanced analysis that teams can do and will hopefully start the conversation that data can
be just as effective of a personnel management tool as coaching and tape.
Disclaimer
This report is intended to be a proof of concept and educational exercise. The views and
opinions expressed in this paper are those of the author and do not represent those of any other party.
Examples of analysis performed within this article are only examples based only on very limited and
dated publicly available information. Any questions or inquiries should be directed to the author at:
eisenmra@gmail.com.
