Which Matters More in the NLL: Penalty Killing or Power Plays?

Which matters more to a team’s overall success? The strength of its penalty killing unit or its power play? 

The question was posed by NLL stat expert Adam Levi on Twitter this Thursday. The comments and replies that flooded into the query offered up a series of interesting theories and possibilities. Could the number of short-handed goals a team allows be a better predictor of a team’s winning percentage than either it’s PK or PP units? What about short-handed goals scored? How about the ratio of short-handed goals scored to a team’s power play goals allowed? 

Truly there are numerous interesting directions this question can lead. But instead of spending time wondering what the relationship of each might look like anecdotally, we set out to answer the question quantitatively. This post will answer the question posed by Levi.

First, we pooled all public NLL statistics from 2005 to 2020 to create a sample size of 166 teams. From there, we overlayed each team’s special teams numbers on their overall records and winning percentages. You can see the full assortment of information below. 

TEAMYEARGPPPGFPPPCTSHGAPPGAPKPCTSHGFWLWin %NET STGGFGAGD
Buffalo2020112250.00%12447.83%574.636213011812
Calgary2020101436.84%32648.00%355.500-1212211111
Colorado2020132655.32%12157.14%676.538101281253
Georgia2020122950.88%32056.52%375.583914912623
Halifax2020123155.36%62556.14%284.667213912613
New England2020112852.83%21958.70%483.7271113510134
New York2020132345.10%32947.27%5112.077-4116177-61
Philadelphia2020142642.62%42256.86%486.571415113417
Rochester2020122244.90%93043.40%3210.167-14115165-50
San Diego2020123055.56%71955.81%666.500101381317
Saskatchewan2020101748.57%22044.44%373.700-21119318
Toronto2020111131.43%31861.70%474.636-612210616
Vancouver2020131939.58%82548.98%449.308-10117160-43
Buffalo2019184648.94%102961.33%6144.7781324418658
Calgary2019184348.86%103160.76%10108.5561221220111
Colorado2019183037.50%62552.83%4612.3333181193-12
Georgia2019183554.69%63551.39%12126.667623021020
New England2019182838.89%124055.06%799.500-17213223-10
Philadelphia2019182838.36%84249.40%7414.222-15218246-28
Rochester2019183645.00%44551.61%8612.333-5212226-14
San Diego2019184147.67%123553.95%6108.5560208217-9
Saskatchewan2019183157.41%83452.11%7117.611-422220220
Toronto2019183647.37%83248.39%12126.66782132076
Vancouver2019183344.00%63956.18%11513.278-1179221-42
Buffalo2018184654.12%104251.72%9810.4443232240-8
Calgary2018184046.51%113753.16%15810.444722721116
Colorado2018184953.26%62955.38%13117.6112721419915
Georgia2018182534.25%133252.24%10117.611-1022621511
New England2018182143.75%63753.75%1499.500-8194242-48
Rochester2018184059.70%74148.75%12108.556423621026
Saskatchewan2018185967.82%113452.78%8144.7782225419658
Toronto2018182736.00%163349.23%7810.444-1523721621
Vancouver2018182337.10%164543.75%8216.111-30186277-91
Buffalo2017184561.64%95645.63%12612.333-8226251-25
Calgary2017185051.02%84650.00%12810.4448212220-8
Colorado2017184155.41%63544.44%999.50092021993
Georgia2017185555.56%93659.55%13135.7222326621353
New England2017182944.62%94750.00%15810.444-12220244-24
Rochester2017183140.26%73851.28%6711.389-8175209-34
Saskatchewan2017184953.85%124446.34%5126.667-223121219
Toronto2017184146.07%124642.50%699.500-1121920019
Vancouver2017184351.81%103647.06%499.5001218221-3
Buffalo2016184150.62%93560.23%12135.722925121437
Calgary2016184852.75%124947.31%12810.444-12162160
Colorado2016183950.65%43247.54%11126.667142032021
Georgia2016185048.54%104847.25%10810.4442238240-2
New England2016184551.72%95745.19%12108.556-922921217
Rochester2016183650.00%64746.59%2711.389-15200215-15
Saskatchewan2016184847.06%83851.90%14135.7221623319043
Toronto2016185752.78%135348.04%5513.278-4190224-34
Vancouver2016184454.32%94948.96%2513.278-12198245-47
Buffalo2015181827.69%64353.26%13117.611-1823620828
Calgary2015184755.29%93664.36%14711.38916212217-5
Colorado2015184349.43%64246.84%999.5004212218-6
Edmonton2015184846.60%102862.67%9135.7221924117764
Minnesota2015182328.75%123851.28%9612.333-18185226-41
New England2015183641.86%193553.95%8414.222-10186249-63
Rochester2015183750.68%53256.16%4126.667420517332
Toronto2015184452.38%43563.92%15144.7782023018545
Vancouver2015184544.12%165244.68%6513.278-17211265-54
Buffalo2014183339.29%104752.04%9810.444-15190200-10
Calgary2014184157.75%63267.68%13126.6671623721522
Colorado2014184247.19%123056.52%8810.4448201228-27
Edmonton2014183741.11%43254.29%9162.8891022015763
Minnesota2014183345.21%93158.11%8414.2221180219-39
Philadelphia2014183643.37%104343.42%6612.333-11202218-16
Rochester2014182648.15%43452.78%8144.778-421016743
Toronto2014184249.41%74252.27%499.500-32192136
Vancouver2014183138.27%83053.13%5414.222-2181223-42
Buffalo2013163444.16%155336.14%7610.375-27171211-40
Calgary2013165668.29%55744.12%1297.563622221111
Colorado2013165060.98%74448.84%779.4386185202-17
Edmonton2013163245.71%53043.40%597.563220317033
Minnesota2013163953.42%84439.73%1479.438121920217
Philadelphia2013163848.72%154146.05%1379.438-5170207-37
Rochester2013162849.12%23258.97%1088.500417916514
Toronto2013165358.24%132463.08%7106.6252319417618
Washington2013163344.59%133844.12%897.563-101931921
Buffalo2012165355.21%145847.27%1279.438-7198204-6
Calgary2012164748.96%64957.76%11124.750321617046
Colorado2012165658.95%94649.45%4115.688521720116
Edmonton2012162532.05%93649.30%11610.375-9167175-8
Minnesota2012165048.54%104451.11%1497.5631020219012
Philadelphia2012164050.63%65446.53%1079.438-10176207-31
Rochester2012163241.56%74345.57%1079.438-8191197-6
Toronto2012165150.00%173656.10%597.56331981962
Washington2012164747.96%73558.33%8412.25013179204-25
Boston2011163543.21%83358.23%788.500116615511
Buffalo2011162633.77%102963.75%6106.625-716915910
Calgary2011164242.86%104357.43%16115.688519818117
Colorado2011164141.00%142563.24%5511.3137151172-21
Edmonton2011162229.73%23456.41%7511.313-7175204-29
Minnesota2011162840.00%73761.46%1188.500-51871807
Philadelphia2011163437.78%73161.25%5511.3131143179-36
Rochester2011162840.00%43957.61%10106.625-517615917
Toronto2011163340.24%62365.67%7106.6251118716819
Washington2011163949.37%113457.50%588.500-12031985
Boston2010162729.67%63563.54%688.500-8161162-1
Buffalo2010163447.89%43957.14%888.500-1169170-1
Calgary2010164245.16%114351.69%7106.625-519316924
Colorado2010162227.50%93147.46%4412.250-14167201-34
Edmonton2010163140.26%53655.56%8106.625-2186201-15
Minnesota2010163652.17%43355.41%6511.3135189201-12
Orlando2010163242.67%73856.82%6115.688-717215418
Philadelphia2010162630.59%84050.00%6511.313-16168194-26
Rochester2010163442.50%83962.50%779.438-6155181-26
Toronto2010165356.38%52260.71%297.5632819715641
Washington2010164253.16%32369.74%10115.6882621117932
Boston2009163941.05%84051.81%6106.625-318116813
Buffalo2009164042.55%73061.04%7106.6251022317053
Calgary2009166860.18%83649.30%10124.7503420616739
Colorado2009164546.88%133559.30%379.4380172184-12
Edmonton2009163546.67%84139.71%7511.313-7159200-41
Minnesota2009163235.96%113652.63%7610.375-8174198-24
New York2009164643.40%54658.93%9106.625419018010
Philadelphia2009163544.87%104652.08%1479.438-7188193-5
Portland2009163040.54%24653.54%397.563-151811774
Rochester2009164248.28%94354.74%879.438-2169197-28
San Jose2009165552.88%95153.21%679.438120018515
Toronto2009163150.00%34852.48%13610.375-7194218-24
Buffalo2008164851.06%84363.87%12106.625920317429
Calgary2008163540.23%123456.96%1179.43801831785
Chicago2008162931.18%104950.00%6610.375-24176212-36
Colorado2008165346.49%64352.75%797.5631118416717
Edmonton2008162734.62%53844.12%2412.250-14141197-56
Minnesota2008164643.40%124159.80%8106.62511991963
New York2008166042.25%134557.14%8106.6251019718611
Philadelphia2008164439.64%165059.68%11106.625-112252205
Portland2008164250.60%94649.45%6610.375-7179194-15
Rochester2008164443.56%73568.75%1988.5002119717126
San Jose2008163843.68%64360.91%1697.563518517213
Toronto2008163640.00%53559.77%379.438-1172174-2
Arizona2007165346.09%84454.64%697.56371881817
Buffalo2007165252.00%75157.85%6106.625020718819
Calgary2007165847.93%115457.48%1897.5631121920217
Chicago2007164637.40%85857.04%15610.375-5176191-15
Colorado2007165846.40%96048.28%15124.750420917930
Edmonton2007164438.60%74459.26%9610.3752160189-29
Minnesota2007165248.15%144951.49%697.563-5200207-7
New York2007165338.41%146544.92%9412.250-17195233-38
Philadelphia2007164038.46%104657.41%4610.375-12178186-8
Portland2007165138.64%166552.55%12412.250-18153199-46
Rochester2007166758.77%125162.50%23142.8752724919455
San Jose2007165644.44%115064.29%1297.563718117011
Toronto2007165545.08%144851.02%6610.375-11871834
Arizona2006163641.38%73064.71%1388.50012198199-1
Buffalo2006165049.02%65459.09%11115.688119316726
Calgary2006163842.22%114062.26%597.563-81831785
Colorado2006166948.25%134262.16%10106.6252420017228
Edmonton2006163128.18%155351.38%9115.063-28150202-52
Minnesota2006164542.86%54253.85%488.5002158171-13
Philadelphia2006163735.58%43264.84%588.50061841840
Portland2006164343.43%115257.38%13115.688-718817711
Rochester2006164945.37%105156.78%897.563-419618016
San Jose2006163330.00%54359.43%10511.313-5151174-23
Toronto2006164942.61%74159.80%688.50071821793
Anaheim2005164139.42%115052.83%10511.313-10175212-37
Arizona2005165244.83%105132.00%1097.56312092090
Buffalo2005164036.70%134864.71%7115.688-1421718334
Calgary2005165051.02%125555.28%10106.625-72162088
Colorado2005164741.59%74361.61%1088.500720118219
Minnesota2005166248.82%95949.14%9511.3133188231-43
Philadelphia2005165547.01%84756.48%11610.37511213218-5
Rochester2005165043.86%145061.54%13106.625-119317914
San Jose2005164435.20%125655.20%5412.250-19170197-27
Toronto2005166151.69%64360.91%17124.7502922719037

With the data sample in hand, figuring out the relationships between various special teams categories and winning percentages was fairly simple. In this case, the relationships we were looking for would be expressed by a standard correlation coefficient. In math and science, the correlation coefficient is called an “r-value”, meaning that we would describe each correlation coefficient as “r = [coefficient]”. Below you can see the r-values of six different variables as they relate to win percentage. As a control, we’ve included goal differential, which clearly has a strong correlation with winning.

r-values

PP CorrelationPK CorrelationSHGA CorrelationSHGF CorrelationNet STGGoal Differential
0.3720.333-0.2060.2460.5550.885

Those numbers are cool, but if you don’t have much experience with correlation coefficients, I’ll take a moment to explain how to interpret what you see. It’s actually super simple and easy to do.

An r-value of 1 is considered a “perfect correlation”, which is something that exists more in theory than in the real world. Inversely, an r-value of 0 is considered to have no correlation whatsoever. Similarly, we seldom see situations where r = 0 in real life. In the real world, correlations are almost never of the “all or nothing” variety. There is nuance to each relationship.

Generally, we consider any r-value above 0.7 as having a “strong positive correlation” whereas an r-value of 0.5 to 0.7 is considered a “moderate positive correlation”, and an r-value of 0.3 to 0.5 is considered a “weak or mild positive correlation”. An r-value from 0 to 0.3 is not considered to have a significant correlation. This same breakdown applies to negative r-values, the difference being the direction of the correlation.

Take Goal Differential as an example. The r-value above says that goal differential and win percentage have a correlation coefficient of 0.885, which we would consider to be a high correlation. As a team’s goal differential goes up, so does its win percentage. In an alternate universe where a rising goal differential translated to a lower win percentage, it would be expressed as a negative r-value (-0.885). But seeing as how only one of the r-values we found is negative, don’t spend too much bandwidth concerning yourself with negative r-values.

Looking to our r-value chart again and applying the breakdown laid out above, we see two categories bearing weak correlations, another pair bearing little to no correlation, and one bearing a moderate correlation.

r-values

PP CorrelationPK CorrelationSHGA CorrelationSHGF CorrelationNet STGGoal Differential
0.3720.333-0.2060.2460.5550.885

These numbers suggest that a team’s power play percentage has a slightly stronger correlation to winning than its penalty kill does. Still, neither offers a particularly strong correlation in general. Additionally, we see that short-handed goals both for and against don’t carry any real correlation to winning at all.

But one area that does have a moderate-to-strong correlation is a team’s “Net Special Teams Goals”, which is described as the following basic formula:

PowerPlay Goals Scored + Short-Handed Goals Scored – PowerPlay Goals Allowed – Short-Handed Goals Allowed

A team’s Net Special Teams Goals is just the overall net of goals scored while a team is on either the power play or penalty kill. It’s an overall total of goals scored during special teams situations, excluding 6-on-5 scenarios. We see that Net STG has an r-value of 0.555, which translates to a moderate positive correlation with win percentage. This is the answer to our question. Individually, penalty killing, power plays, and short-handed goals bear no correlation with winning. It isn’t until we consider them collectively that a true correlation emerges.

When you think about it for a second, this probably shouldn’t be a surprising conclusion. A team with a positive Net STG is probably going to have a positive overall goal differential, which we know correlates strongly with winning.

So which one is it? Power play or penalty kill? Technically, the answer is that power play success (r = 0.372) correlates to winning more strongly than the penalty kill (r = 0.333) does. But really, neither carry all that much weight individually. Elite teams are good on both ends of the floor usually, and special teams is no exception to that pattern, which is why the only viable correlation belongs to Net Special Teams Goals.

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