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

data does not exist on the subject.

4 Possessions = .96*(Field Goals Attempted – Off Rebounds + Turnovers + (.44 * Free Throws Attempted))

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

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

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 75th

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.

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.

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 0.11 0.12 0.13 0.14 0.15% of Team Possesions Used

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

Total Points

Stanwick, Steele1.586

Glesener, John1.326

Schreiber, Tom1.177

Mackrides, Matt1.085

Walters, Joe1.022

Danowski, Matt0.943

Rabil, Paul0.906

Powell, Casey0.897

Westervelt, Drew0.895

Kimener, Terry0.891

Grant, John0.867

Bocklet, Michael0.860

Wolf, Jordan0.845

Staats, Randy0.837

Thompson, Lyle0.830

Holman, Marcus0.794

Chanenchuk, Mike0.781

McArdle, Kieran0.769

Sieverts, Jeremy0.763

Rubeor, Ben0.734

Sankey, Joey0.720

Cunningham, Kevin0.707

Pannell, Rob0.694

Gibson, Matt0.674

Rice, Jack0.673

Abbott, Matt0.639

Emala, David0.638

Law, Eric0.633

Lawson, Dave0.608

Crowley, Kevin0.574

Snider, Drew0.572

Brooks, Steven0.559

Ranagan, John0.533

Seibald, Max0.422

Hawkins, Josh0.298

Drew, Kevin0.230

TOP LAX IMPACT! Players

Team BOS CHA CHE DEN FLA NY OH ROC