[Preface]
Unlike America's primary three sports, lacrosse has received little attention in terms of statistical development and methodology. Those statistics that have dominated the game since the inception of its recordation are still at the forefront of examining production and value.

Unfortunately, those traditional methods have become dated and I think there are better methods for determining production. The methodology I am proposing in this essay is similar to much of the material I have presented on football and basketball, where an eye is turned toward possession-based statistics rather than game or season-based averages.

Possession-based statistics, as opposed to traditional statistical approaches, provide a truer examination of efficiency, whether offensive or defensive. Analyzing team or player efficiency is preferable to traditional "averages" analysis because efficiency illustrates an individual's or team's consistency. Traditional statistics, however, tend to reward an entity for a particularly impressive performance and having that output residually imputed to underachieving performances. This kind of inaccuracy is unacceptable.

[Methodology]
As noted above, I am proposing in this essay the use of a possession-based method to examine a team's offensive and defensive efficiency. However, the implementation of this system is not without difficulty.

Unlike many college basketball teams, there are not, currently, any college lacrosse teams that chart possessions. Consequently, to determine the number of possessions a team generates in a contest, we need to find those events in the boxscore that mark the end or beginning of a given possession. As it turns out, there are essentially two ways for a possession to end or begin:

1. On the Face-Off
Every possession following a goal or the start of a new period begins with a center face-off. When any player successfully corralls the loose ball following the face-off, possession is awarded to that team.

Thus, the first element in the equation is: (Face-offs Won)

2. Clearing Attempts
Defining when a possession begins or ends on a clearing attempt is not as easy to recognize as it is on a face-off. Because a clearing attempt may or may not be successful, there is actually the opportunity for two possessions to begin and two possessions to end. To illustrate this point, and example is necessary.

Let's say Team A wins the face-off against Team B and advances the ball into Team B's defensive zone. This simple action - collecting the ground ball and advancing it into the attack zone - results in one possession for Team A.

As Team A works the ball around Team B's defensive zone looking for an opportunity, Team B intercepts an errant pass. Consequently, Team B now has a clearing opportunity as a result of the turnover. This clearing opportunity marks a change of possession, no matter how many shots Team A took on change, so long as none of Team A's shot tickled the twine. Thus, the possessions at this point are each 1 and 1, despite the fact that Team B has yet to advance the ball out of their own defensive side of the field.

Now, this is where the possession analysis gets tricky. Given the fact that once Team B creates a clearing opporunity a new possession occurs, every clearing attempt must be considered a team possession, regardless of whether the team is successful in clearing the ball from their defensive zone. Because all clearing attempts are considered possessions, Team A actually has the opportunity to create two possessions without the ball leaving Team B's defensive zone.

So, say Team B intercepts a Team A pass as noted earlier. On the clearing attempt, Team B fails to clear the ball either by an errant pass, a Team A interception, or a failure to proceed in the required amount of time. At that point, Team A will gain possession.

Thus, from the face-off to Team B's failed clear, three separate possessions have taken place - two for Team A and one for Team B.

If you are asking yourself why clearing attempts are considered as marking the end or beginning of a possession instead of turnovers, you're asking a very valid question. However, turnovers, unlike in basketball, are not effective means for defining when a change of possession takes place in lacrosse. This is for two reasons.

First, very few college lacrosse teams track turnovers. To formulate an equation that takes into account a statistic that appears in only a handful of boxscores is not an effective means for determining the number of possessions a team has.

Second, clearing attempts naturally take into account turnovers, and to consider turnovers as a separate category would "double count" the statistic. In other words, a turnover naturally leads to either a clearing attempt or a failed clearing attempt. Thus, all "turnovers for" are counted for in "total clearing attempts" and "failed opponent clearing attempts."

As a consequence of all of this, the formula for possessions now looks as follows:

Face-offs Won + Total Team Clearing Attempts + Number of Failed Opponent Clearing Attempts

[Example]
To illustrate how the possession formula works, a brief look at Syracuse's 2005 statistics may be helpful.

Face-Offs Won:
Syracuse - 175
Opponents - 158

Total Clearing Opportunities:
Syracuse - 286
Opponents - 283

Total Failed Clearing Opportunities:
Syracuse - 45
Opponents - 56

Thus, with this material, we can determine the number of possessions Syracuse had in 2005 by using the formula above:

175+286+56 = 517 = Number of Syracuse Possessions
158 + 283 + 45 = 486 = Number of Syracuse Opponent Possessions

This data indicates that Syracuse averaged somewhere around 38 possessions per game last season. The Orange's opponents averaged about 36, which makes sense, given the fact that Syracuse was stronger on the ride and at the face-off "x" than its opponents, thus resulting in more possessions per contest.

[Data]
The data produced below represents Offensive Efficiency (goals/possession), Defensive Efficiency (opponent goals/possession), and Efficiency Margin (Offensive Efficiency - Defensive Efficiency).

Note: This data does not include Vermont because I have not been able to uncover data from its games against Duke or St. Michael's.

* = Reclassifying/Provisional Division I member

Offensive Efficiency
Rank	Team	OEff
1	Duke	.3499
2	Cornell	.3313
3	Johns Hopkins	.3169
4	Bellamarine*	.3157
5	Massachusetts	.314
6	Bucknell	.3034
7	Syracuse	.2998
8	Albany	.2934
9	Colgate	.2906
10	Delaware	.2875
11	Princeton	.2812
12	Notre Dame	.2799
13	Maryland	.2796
14	UMBC	.2791
15	Stony Brook	.2783
16	Lehigh	.274
17	Penn State	.2718
18	Manhattan	.2701
19	Navy	.2684
20	Denver	.268
21	Mt. St. Mary's	.2677
22	Dartmouth	.265
23	Virginia	.2641
24	Army	.2639
25	Holy Cross	.2623
26	Yale	.2582
27	VMI	.2577
28	Ohio State	.2573
29	Fairfield	.2564
30	Drexel	.2552
31	North Carolina	.2546
32	Hofstra	.2546
33	Brown	.25
34	Georgetown	.2488
35	Sacred Heart	.2457
36	Towson	.2426
37	Rutgers	.2415
38	Hobart	.2404
39	Air Force	.2383
40	Providence	.2332
41	Loyola (Md)	.2304
42	Pennsylvania	.227
43	Harvard	.2227
44	Marist	.2206
45	Quinnipiac	.219
46	Hartford	.2144
47	Siena	.214
48	Canisius	.2103
49	Villanova	.2074
50	Butler	.2027
51	St. Joseph's	.1891
52	Binghamton	.1869
53	Robert Morris	.1845
54	Wagner	.1724
55	St. John's	.1605
56	Lafayette	.1404

Defensive Efficiency
Rank	Team	DEff
1	Bellamarine*	.1843
2	Virginia	.1878
3	Duke	.1963
4	Denver	.1966
5	Navy	.2035
6	Drexel	.2101
7	Villanova	.211
8	Cornell	.2155
9	Notre Dame	.2156
10	Brown	.2194
11	Georgetown	.2206
12	Binghamton	.2241
13	Bucknell	.2244
14	Dartmouth	.2287
15	Johns Hopkins	.2298
16	Loyola (Md.)	.2304
17	North Carolina	.2343
18	Marist	.2354
19	Canisius	.2367
20	Hofstra	.2377
21	Stony Brook	.2388
22	Towson	.2428
23	Albany	.2441
24	Massachusetts	.2455
25	Lehigh	.2474
26	Maryland	.2487
27	Princeton	.2493
28	Fairfield	.2516
29	Air Force	.253
30	Wagner	.2576
31	St. John's	.2583
32	Delaware	.259
33	Army	.2625
34	UMBC	.2631
35	Harvard	.2645
36	St. Joseph's	.2647
37	Providence	.265
38	Manhattan	.2747
39	Hobart	.2751
40	Siena	.2786
41	VMI	.2788
42	Yale	.2802
43	Syracuse	.2819
44	Rutgers	.2826
45	Mt. St. Mary's	.2838
46	Butler	.2849
47	Sacred Heart	.287
48	Colgate	.2871
49	Lafayette	.2887
50	Quinnipiac	.2932
51	Holy Cross	.294
52	Penn State	.2942
53	Ohio State	.2971
54	Pennsylvania	.3189
55	Robert Morris	.3216
56	Hartford	.3263

Efficiency Margin
Rank	Team	EffMar
1	Duke	.1536
2	Bellamarine*	.1314
3	Cornell	.1158
4	Johns Hopkins	.0871
5	Bucknell	.07897
6	Virginia	.0763
7	Denver	.0715
8	Massachusetts	.0685
9	Navy	.0649
10	Notre Dame	.0643
11	Albany	.0493
12	Drexel	.0451
13	Stony Brook	.0395
14	Dartmouth	.0363
15	Princeton	.0318
16	Maryland	.0309
17	Brown	.0306
18	Delaware	.0285
19	Georgetown	.0282
20	Lehigh	.0266
21	North Carolina	.0204
22	Syracuse	.0179
23	Hofstra	.0169
24	UMBC	.0160
25	Fairfield	.0048
26	Colgate	.0035
27	Army	.0014
28	Loyola	.0000
29	Towson	-.0002
30	Villanova	-.0036
31	Manhattan	-.0046
32	Air Force	-.0147
33	Marist	-.0148
34	Mt. St. Mary's	-.0161
35	VMI	-.0211
36	Yale	-.02199
37	Penn State	-.0225
38	Canisius	-.0264
39	Holy Cross	-.0317
40	Providence	-.0318
41	Hobart	-.0347
42	Binghamton	-.0372
43	Ohio State	-.0398
44	Rutgers	-.0411
45	Sacred Heart	-.0413
46	Harvard	-.0418
47	Siena	-.0646
48	Quinnipiac	-.0742
49	St. Joseph's	-.0756
50	Butler	-.0822
51	Wagner	-.0852
52	Pennsylvania	-.0919
53	St. John's	-.0978
54	Hartford	-.1118
55	Robert Morris	-.1371
56	Lafayette	-.1483

[Closing Remarks]
Unlike much of the efficiency material I have previously written about in this notebook, the formulae and data derived for this particular essay has been developed by yours truly. Therefore, I am interested in fielding comments as to whether the information I have used to develop possession data is sound.

So, if you feel the urge to comment on any of the methods used above, you're more than welcome to leave a comment below.