Ranking Every NLL Position Player: Introducing the Weighted Average

The motivation behind LaxMetrics.com is to unearth and develop new ways of evaluating player performance. As is evident across the site, there is no shortage of methods for evaluating offensive players against each other. Whether it’s drawing from existing stats or tracking new categories, we have ample inputs available to which we can apply some arithmetical magic. The same is true of defense/transition players and goalies, only to a lesser extent. Truthfylly, it’s especially difficult to measure the performances of defensive/transition players objectively.

It begs the question: how can we strip away positions and compare players out the front door to their teammates out the back door? Is it possible to use numbers to create an apples-to-apples comparison across all positions on the floor? In this LaxMetrics blog entry, we’re going to attempt to do exactly that.

First, we are going to introduce you to a new kind of metric—the LaxMetrics Weighted Average. Then once we’ve established the origins of the Weighted Average and how it works, we will group players into three tiers based on their score distributions. And lastly, we are going to highlight a few interesting cases that may support conventional wisdom or may come as a surprise.

Let’s get into it.

At its core, the LaxMetrics Weighted Average is just a collection of a bunch of individual statistics pooled together, rated by their importance, and then averaged out. Whereas a typical average considers each input equally, a weighted average allows us to assign different orders of magnitude (weights) to each input. In this case, the advantage in employing a weighted average as opposed to a classic average is that we can curate which stats we think should be considered more important than others. After all, it would be silly to value short-handed goals and loose balls the same. One is far more common than the other, so weighting them the same would void any nuance of scarcity that exists around short-handed goals. That would be a waste of time, right?

While we’ll use the Weighted Average to compare players across positions, the formula that we use actually depends on the player’s position. One formula is applied to forwards, while another is used for defense/transition guys. Despite the formulas being different, the outputs that they offer should be comparable. Below are the lists of inputs we used. The first grouping, applied to forwards, is labeled “O Weighted Average” and the second grouping, applied to defense/transition players, is labeled “D Weighted Average”. The weights of each input are listed in parentheses next to their correlating inputs.

O Weighted Average: Even Strength G (2), PPG (1.5), SHG (3), PD (1), PA (2), UA (1), FoA (2), SoA (1), GoE (1)

D Weighted Average: Even Strength G (1), PPG (0.75), SHG (3), PD (1), LB (0.25), CTO (2.5), UA (0.25), FoA (1.5), GoE(1)

As you can see, seven of the nine inputs are the same across the two weighted averages. The differences are that the O Weighted Average includes Pick Assists and Second Order Assists, while the D Weighted Average includes Loose Balls and Caused Turnovers. Additionally, Even Strength Goals, PowerPlay Goals, Unrealized Assists, and First Order Assists are assigned different weights in each average. Because defense/transition players are not primarily goal scorers and shot creators, it isn’t appropriate to suggest that they deserve the same gravity as things like Caused Turnovers in evaluating defensive performances. If we were to weight Even Strength Goals and PowerPlay goals the same in each average, we would unintentionally suppress D Weighted Average scores, ruining our effort at an apples-to-apples comparison.

The only scoring categories that are weighted the same in each average are Short-Handed Goals and Goals Over/Under Expectations. The weights assigned are the same because both stats are roughly the same across positions. For example, league-wide, there isn’t a tremendous difference between the number of shorties scored by forwards as opposed to defense/transition players. The same can’t be said for other inputs like Even Strength Goals, PowerPlay Goals, Unrealized Assists, and First Order Assists. Those four categories are inherently offense-centric, meaning that the distribution is disproportionately tilted toward forwards. The inverse is true of Caused Turnover and Loose Balls, which are generally slanted heavily toward defense/transition guys.

As a cumulative statistic that grows as a player accumulates more relevant stats, season totals are a viable tool for comparing players to each other. Weighted Average Per Game can also be useful, but won’t be the primary method of comparison used by the LaxMetrics blog.

Now onto the players and their scores.

Below you can see a list of the top-30 NLL players ranked by their total Weighted Average scores through Week 16. Keep in mind that goalies are excluded from these rankings, so this can’t be considered a true top-30 list. 

RANKPlayerTeamWeighted AverageWeighted Average/Gm
1Joe ResetaritsALB13.120.937
2Dhane SmithBUF12.351.123
3Zach CurrierCGY12.141.103
4Lyle ThompsonGEO11.770.906
5Mark MatthewsSAS11.000.846
6Jeff TeatNY10.471.163
7Robert ChurchSAS10.290.791
8Keegan BalVAN10.260.932
9Ryan LeeCOL10.010.834
10Jesse KingCGY9.680.880
11Mitch de SnooTOR9.670.806
12Wesley BergSD9.650.878
13Ryan BeneschALB9.270.618
14Brad KriTOR9.090.758
15Brendan BomberryGEO9.050.754
16Reid BoweringVAN9.030.753
17Tom SchreiberTOR8.870.887
18Jeremy NobleSD8.730.873
19Callum CrawfordNY8.650.787
20Will MalcomPCLC8.610.718
21Shayne JacksonGEO8.520.710
22Dane DobbieSD8.480.771
23Kiel MatiszPHI8.450.704
24Kevin CrowleyPHI8.390.699
25Kyle RubischSAS8.370.644
26Josh ByrneBUF8.110.738
27Eli McLaughlinCOL7.970.664
28Connor RobinsonCOL7.950.662
29Mike Messenger*SAS7.940.611
30Jake Withers*HFX7.940.722

It doesn’t take much digging to notice that the top-30 is still heavily slanted toward forwards. Of the group, 21 are forwards, while only 9 would be classified as defense/transition players. While this might reflect the imperfection in our attempt at creating an apples-to-apples comparison metric, the LaxMetrics blog might argue that there are more truly elite, game-changing players on offense around the league than there are out the back door. Looking at the list, virtually every team in the league has at least one star forward included in the Weighted Average top-30. Additionally, the breakdown begins to balance in the grouping of players from 31-60 in which nearly half (13) are defense/transition players.

Looking at the graph below, we can see a distribution breakdown of all of the Weighted Average scores league-wide.

There is a fairly clear demarcation between what we would consider Tier 1 and Tier 2. The drop-off from Tier 2 to Tier 3 is even steeper. Tier 1 is comprised of only 13 players, which is just 4.1% of the 317 total position players ranked by Weighted Average. This means that in order to qualify as a Tier 1 member, a player has to rank in the 96th percentile or better. These players are truly having elite seasons.

Tier 2, however, is far larger. The group is comprised of 48 players, which amounts to 15.1% of all players ranked. This means that in order to qualify as one of the 61 players in Tier 1 & Tier 2 combined, a player has to score in the top 20% of all Weighted Averages. If Tier 1 is comprised of the league’s elite, Tier 2 is made up of the very, very good.

Tier 3, which includes all other players, contains a wide range from the league’s least productive players ranging toward a group of very good players at the top. The reason we don’t break Tier 3 into additional tiers is that the graph illustrates how clear the groupings are. At the border of each Tier is a steep drop-off in score distribution. This means that there are roughly as many very good players in Tier 3 as there are very unproductive players. For a player to fall into Tier 3 isn’t necessarily an indictment on his performance, it is just an illustration of where he stands compared to the league’s best. 

Below is the full 318-player list, which you can sort by either total Weighted Average or Weighted Average Per Game:

RANKPlayerTeamWeighted AverageWeighted Average/Gm
1Joe ResetaritsALB13.120.937
2Dhane SmithBUF12.351.123
3Zach CurrierCGY12.141.103
4Lyle ThompsonGEO11.770.906
5Mark MatthewsSAS11.000.846
6Jeff TeatNY10.471.163
7Robert ChurchSAS10.290.791
8Keegan BalVAN10.260.932
9Ryan LeeCOL10.010.834
10Jesse KingCGY9.680.880
11Mitch de SnooTOR9.670.806
12Wesley BergSD9.650.878
13Ryan BeneschALB9.270.618
14Brad KriTOR9.090.758
15Brendan BomberryGEO9.050.754
16Reid BoweringVAN9.030.753
17Tom SchreiberTOR8.870.887
18Jeremy NobleSD8.730.873
19Callum CrawfordNY8.650.787
20Will MalcomPCLC8.610.718
21Shayne JacksonGEO8.520.710
22Dane DobbieSD8.480.771
23Kiel MatiszPHI8.450.704
24Kevin CrowleyPHI8.390.699
25Kyle RubischSAS8.370.644
26Josh ByrneBUF8.110.738
27Eli McLaughlinCOL7.970.664
28Connor RobinsonCOL7.950.662
29Mike Messenger*SAS7.940.611
30Jake Withers*HFX7.940.722
31Ryan DilksSAS7.920.609
32Liam ByrnesPCLC7.680.590
33Shawn EvansHFX7.660.639
34Patrick DoddsPCLC7.650.637
35Rob HellyerTOR7.540.686
36Jordan MacIntoshGEO7.350.565
37Andrew KewALB7.200.600
38Matt RamboPHI7.000.584
39Connor KearnanNY6.950.631
40Clarke PettersonHFX6.860.686
41Holden CattoniROC6.850.623
42Dan CraigTOR6.800.567
43Matt HossackPCLC6.770.564
44Challen RogersTOR6.740.562
45Curtis DicksonCGY6.700.837
46Robert HopeCOL6.610.551
47Corey SmallPHI6.580.598
48Austin StaatsSD6.360.795
49Ryan KeenanSAS6.360.489
50Chad TuttonGEO6.240.520
51Owen BarkerVAN6.200.564
52Tyler PaceCGY6.200.775
53Tehoka NanticokeBUF6.190.562
54Jeremy Thompson*PCLC6.130.511
55Dan DawsonTOR6.010.601
56Trevor Baptiste*PHI5.980.499
57Scott DomineyNY5.960.542
58Holden GarlentSAS5.960.458
59Brodie MerrillSD5.950.595
60Jordan HallGEO5.840.449
61LaTrell HarrisTOR5.830.729
62Chris WardleCOL5.730.477
63Ryan SmithROC5.700.518
64Brett HickeySD5.620.432
65Reid ReinholdtTOR5.560.556
66Austin ShanksHFX5.550.555
67Matt BeersSAS5.540.426
68Anthony JoaquimCOL5.530.425
69Jacob RuestALB5.510.394
70Thomas HoggarthROC5.460.496
71Ben McIntoshPHI5.430.452
72Steve PrioloBUF5.310.590
73Joey CupidoCOL5.290.529
74Chris CloutierBUF5.260.478
75Cody JamiesonHFX5.180.575
76Ian MacKayBUF5.180.518
76Ryland ReesROC5.180.431
78Jordan MacIntoshGEO5.140.429
79Reilly O'ConnorALB5.140.396
80Connor FieldsBUF5.130.466
81Adam WiedemannGEO5.100.392
82Logan ShussVAN5.070.507
83Jackson NishimuraALB5.060.362
84Tony MalcomALB5.060.460
85Brett MydskeVAN5.040.420
86Joel WhiteGEO5.040.387
87Eli GobrechtSD4.990.454
88Dan McRaeNY4.970.452
89Kyle KillenVAN4.830.439
90Nathan GrenonPCLC4.790.436
91Phil CaputoPCLC4.790.399
92Dan LintnerSAS4.770.367
93Kieran McArdleNY4.720.429
94Jeff ShattlerSAS4.710.428
95Brandon GoodwinVAN4.690.390
96Joe Nardella*ALB4.640.332
97Curtis KnightROC4.630.386
98TJ ComizioGEO4.630.386
99Tyler Burton*CGY4.620.578
100Blaze RiordanPHI4.600.383
101Greg DowningALB4.560.414
102Bobby KiddSAS4.530.378
103Pat FoleyPCLC4.530.348
104Mitch JonesVAN4.521.130
105Chris CorbeilSAS4.510.376
106Stephen LeblancGEO4.500.409
107Riley LoewenVAN4.500.409
108Adam BomberryALB4.490.641
109Jeff CornwallSAS4.460.372
110Chris BoushyHFX4.410.401
111Bryan ColeGEO4.400.367
112Kyle BuchananBUF4.400.440
113Zed WilliamsCOL4.370.397
114Tre LeclaireSD4.350.396
115Josh MedeirosPCLC4.330.361
116Graeme HossackHFX4.270.388
117Colton WatkinsonALB4.270.305
118Scott CarnegieCOL4.160.347
119Turner EvansROC4.140.345
120Larson SundownNY4.120.375
121Karson TarbellGEO4.090.315
122Connor SellarsPCLC4.020.402
122John LaFontaineALB4.020.287
124Alex PacePHI4.020.365
125Billy HostrawserTOR4.010.335
125Taylor StuartVAN4.010.309
127Ryan TerefenkoHFX3.990.443
128Patrick ShoemaySD3.970.397
129Marty DinsdaleVAN3.940.358
130Eric FannellHFX3.890.354
131Kevin BrownellBUF3.850.385
132Chase FraserBUF3.840.349
133Jordan GillesCOL3.840.384
134Tyson BellHFX3.820.382
135John LintzCOL3.820.318
136Tyler DigbyNY3.750.375
137Kyle JacksonHFX3.720.372
138Dan TaylorCGY3.710.371
139Nick ChaykowskyALB3.700.309
140Matt GilrayROC3.670.306
141Mike ByrneALB3.670.282
142Curtis ManningCGY3.630.403
143Charlie KitchenALB3.600.300
144Josh JubenvilleTOR3.590.299
145Adam CharlambidesVAN3.580.298
146Liam LeClairCGY3.530.321
147Alex CrepinsekPHI3.510.319
148Matt SpangerBUF3.480.386
149Jake FoxNY3.450.314
150Cam HoldingSD3.410.379
151Matt MarinierNY3.380.422
152Zach MannsTOR3.370.375
153Connor McClellandSAS3.330.278
154Ryan WagnerPHI3.320.302
155Darryl RobertsonNY3.300.300
156Justin SaltVAN3.300.300
157Cam MilliganPCLC3.281.094
158Charlie BertrandROC3.280.273
159Zack GreerSD3.270.327
160Dean FairallPCLC3.260.652
161Garrett McIntoshVAN3.260.271
162Damon EdwardsNY3.250.325
163Paul DawsonROC3.170.264
164Adrian SorichettiALB3.160.226
165Trevor SmythHFX3.150.286
166Thomas WhittyROC3.140.285
167Liam PattenPCLC3.130.285
168Jay Thorimbert*NY3.120.284
169Jalen ChasterCOL3.090.309
169Scott CampbellHFX3.090.309
171Steph CharbonneauPHI3.080.280
172Ethan WalkerGEO3.000.300
173Zach HerreweyersCGY3.000.333
174Mac O'KeefeSD2.990.271
175Graydon BradleySD2.970.297
176Tyson GibsonCOL2.950.227
177Tor ReinholdtSD2.860.318
178Tanner CookCGY2.830.258
179Jamison DilksTOR2.820.235
180Chad CummingsPCLC2.820.235
181Mike TrioloPCLC2.720.340
182Brad GilliesHFX2.680.268
183Cory VitarelliPHI2.650.378
184Josh CurrierSAS2.620.219
185Dan CoatesROC2.620.218
186Ryan MacSpadyenGEO2.610.201
187Eli SalamaCGY2.600.289
188Brandon SladeTOR2.570.257
189Brett ManneyALB2.560.213
190Phil MazzucaTOR2.550.319
191Shane SimpsonCGY2.530.316
192Colton ArmstrongHFX2.510.228
193John RanaganPHI2.500.209
194Dawson TheedeHFX2.490.415
195Harrison MatsuokaCGY2.410.219
196TD Ierlan*TOR2.390.239
197Travis CornwallPCLC2.380.216
198Kyle WatersCGY2.350.294
199Haiden DicksonCGY2.330.292
200Matthew BennettROC2.270.227
201Bryce TolmieNY2.250.374
202Bryce SweetingBUF2.190.219
203Isaiah Davis-AllenPHI2.180.182
204Drew BelgraveSD2.140.238
205Ethan TicehurstCGY2.120.193
206Justin MartinBUF2.100.233
207Tyrell Hamer-Jackson*VAN2.060.229
208Mackenzie MitchellNY2.060.228
208Chris WillmanROC2.060.206
208Reece CalliesCGY2.060.206
208Warren JeffreyCOL2.060.187
212Taite CattoniPCLC2.040.409
213Ron JohnNY1.930.193
214Nick Weiss*BUF1.910.212
215Jeff HenrickGEO1.910.191
216Mark GliciniSD1.900.238
217Stephen KeoghHFX1.900.316
218Andrew BorgattiNY1.880.188
219Ethan O'Connor*BUF1.860.186
220Matt SykesSD1.830.183
221Anthony KalinichVAN1.790.149
222Austin MurphySAS1.690.338
223Cory HighfieldROC1.630.204
224Dylan KinnearCOL1.630.233
225Justin RobinsonBUF1.580.158
226Leo StourosNY1.570.175
227Tim Edwards*COL1.530.219
228Brandon Clelland*SD1.490.186
229Dylan HutchinsonPCLC1.430.159
230Jordan McBrideVAN1.420.237
231Tanner ThomsonALB1.380.173
232Tyler BilesROC1.360.114
233Matt BoissonneaultSD1.360.339
233Tyler CodronVAN1.360.113
235Casey Jackson SD1.340.334
236Derek LloydVAN 1.310.187
237Josh SullivanCOL1.300.108
238Chris WeierTOR1.280.319
239Curtis ConleyALB1.270.212
240Brent Noseworthy*NY1.250.179
241Sam LeClairPHI1.200.301
242Robert HudsonGEO1.140.088
243Andrew MullenCGY1.140.190
244Jordan TrottierPCLC1.100.110
245Connor WatsonHFX1.090.365
246Ryan FournierNY1.020.113
247Doug UttingROC0.970.194
248Ian LlordPHI0.950.106
249Tyler HallsROC0.920.231
250Nate WadeCGY0.920.153
251Sheldon BurnsTOR0.910.091
252Jack JasinskiPCLC0.890.447
253Jordan DurstonALB0.850.095
254Ryan MartelVAN0.830.166
255Jordan StourosBUF0.810.162
256Nonkon ThompsonHFX0.810.161
257Connor KirstGEO0.760.059
258Carter McKenzieCGY0.750.150
259Luc MagnanHFX0.750.150
259Ethan RiggsGEO0.750.075
261Mitch OgilvieROC0.700.234
262Tyler GarrisonSD0.680.227
263Dan LomasNY0.670.336
264Jackson SubochPHI0.640.160
265Justin ScottTOR0.630.125
266Ryan McLeanPCLC0.620.088
267Rhys DuchHFX0.610.307
268Marshall PowlessSAS0.580.116
269Adam JayTOR0.580.116
270Mike MallorySAS0.570.072
271Kellen LeClairCGY0.570.286
272Mike BurkeROC0.570.571
273David BrockHFX0.560.093
274Mitch WildeCGY0.550.185
275Erik TurnerCOL0.470.043
276Tanner BuckGEO0.470.233
277Jordi Jones-SmithSAS0.460.042
278Alex Woodall*COL0.410.135
279Tyson RoeVAN0.360.089
280Marshal King CGY0.350.175
281Harrison SmithPCLC0.340.336
282Vaughn HarrisGEO0.330.084
283Travis LongboatGEO0.320.319
283John WagnerROC0.320.040
285Brad McCulleyBUF0.270.066
286Devyn MayeaSD0.250.254
287Keegan BellVAN0.250.247
288Tyson BomberrySD0.250.082
289Max AdlerBUF0.230.057
290Jacob DunbarSD0.210.106
291Jordan McKennaTOR0.180.184
292Jason NobleTOR0.160.032
293Aaron ForsterTOR0.140.048
294Blake Gibson-McDonaldPHI0.140.069
295Brad VoigtGEO0.110.056
296Jeff WittigROC0.100.020
297Luke Van SchepenNY0.100.048
298Colton PorterVAN0.090.095
298Eric ShewellPHI0.090.016
300Ty ThompsonNY0.090.044
301Johnny PearsonPCLC0.070.065
301Tristan RaiSAS0.070.022
303Mikie SchlosserSD0.060.029
304Curtis RomanchychALB0.050.051
305Thomas SempleGEO0.030.010
306Brayden HillHFX0.030.029
306Oliver BolsterliSD0.030.029
308Dalton SulverBUF0.00-0.002
308Clark WalterSAS0.00-0.004
310Marcus MinichielloBUF-0.01-0.014
311Brad SmithALB-0.02-0.019
311Sam La RoueBUF-0.02-0.019
313Liam PhillipsPCLC-0.03-0.031
314Kyle MarrPHI-0.03-0.034
315LeRoy HalftownNY-0.05-0.049
316Mark CockertonTOR-0.06-0.064
317Cole PickupCGY-0.65-0.326

To offer a little bit of added context, the league average Weighted Average Per Game is 0.345 and the median total Weighted Average is 3.27, which we would describe as roughly being the “middle” of all 317 scores. 

When we compare individual players to the median, it puts into perspective just how excellent both Tier 1 and Tier 2 players have been. Each of the 61 players that comprise Tiers 1 & 2 score significantly higher than the median, often more than double its value. For example, Toronto’s LaTrell Harris is the final player in Tier 2, posting a Weighted Average of 5.83, roughly 178% of the median. Following Harris, there are still 97 players who have scored above the median, but are confined to Tier 3.

But through all of this, who are the outliers whose positions in the rankings might come as a surprise? One example is Toronto’s Challen Rogers. While he is still a Tier 2 player, he doesn’t rank among the league’s elite transition players. His overall position of #44 on the Weighted Average list places him clearly behind the primary group of contenders for Transition Player of the Year, an award that Rogers has dominated recently. In fact, Rogers ranks behind both Mike Messenger from Saskatchewan and Panther City’s Matt Hossack, a pair of players making a relatively recent ascent into the “league best” transition conversation.

Perhaps even more surprising than Rogers’s position is that of Colorado’s Joey Cupido. A former Transition Player of the Year himself, Cupido ranks outside the top two tiers at #73 overall. Statistically, Cupido has taken a bit of a step back this year, despite still being an impact player. While Cupido’s Weighted Average is held down by his relative lack of loose balls and limited point total, the Mammoth are still 5-1 when he records a point.

In another mildly surprising realization, a pair of Saskatchewan forwards rank among the league’s 10 best players, despite the Rush having featured one of the league’s least impressive offenses. Between Mark Matthews (#5) and Robert Church (#7), the Rush are the only team with two forwards in the top-10. In fact, the only other team with two players in the top-10 is the Roughnecks, who boast Zach Currier at #3 and Jesse King at #10. It’s crazy to think that two of the league’s worst teams combine to feature four of the league’s top-10 players by Weighted Average. Saskatchewan and Calgary have plenty of problems, but star power certainly is not one of them.

Interestingly, the current state of the LaxMetrics Weighted Average rankings aligns quite well with much of the conversation about postseason awards. Much of the MVP conversation has centered around the top three forwards on the list: Albany’s Joe Resetarits, Buffalo’s Dhane Smith, and Georgia’s Lyle Thompson. For good measure, dark horse MVP candidate Zach Currier is visibly present at the top. 

Similarly, the top four defense/transition guys in the Weighted Average rankings are Currier, Vancouver’s Reid Bowering, and Toronto’s Mitch de Snoo and Brad Kri in that order. If you were to ask most media people around the league who their finalists for the Transition Player of the Year would be at this point, most would return an answer including three of those four players.

The conversation around the Rookie of the Year is also reflected quite clearly in the Weighed Average rankings. The hierarchy seems to follow what most of the debate suggests: a top-four of New York’s Jeff Teat, Bowering, Panther City’s Patrick Dodds, and Buffalo’s Tehoka Nanticoke in that order. You’d have a hard time finding anyone around the league whose RoY finalists would include someone other than that top four.

While it is only our first true attempt at creating an apples-to-apples comparison metric, there still is inevitably room for improvement in optimizing the inputs and their weights. Surely it can be improved upon in the future, but its current alignment with most of popular opinion bodes well for the accuracy of its rankings. Inevitably the Weighted Average will give way to an even more meticulous positionless ranking of players, but for now we can use it to explore some of the NLL’s landscape. Numbers and the eye test don’t always agree, but it’s particularly interesting when they do. In this case, the duo is more in sync than not.

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