Jul 112012
 

I have been wondering about the benefits of using 5v5 close data instead of 5v5 when we do player analysis and player comparisons.  The rationale for comparing players in 5v5close situations is that we are comparing players under similar situations.  When teams have a comfortable lead they go into a defensive shell resulting in fewer shots for but with a higher shooting percentage and more shots against, but a lower shooting percentage.  The opposite of course is true when a team is trailing.  But what I have been thinking about recently is whether there is a quality of competition impact during close situations.  My hypothesis is that teams that are really good will play more time with the score close against other good teams and less time with the score close against significantly weaker teams.  Conversely, weak teams will play more minutes with the score close against other weak teams than against good teams.

My hypothesis is that players on good teams will have a tougher QoC during 5v5 close situations than during overall 5v5 situations and players on weak teams will have weaker QoC during 5v5 close situations than during overall 5v5 situations.  Let’s put that hypothesis to the test.

The first thing I did was to select one key player from each of the 30 teams to represent that team in the study.  Mostly forwards were chosen but a few defensemen were chosen as well.  From there I looked at the average of their opponents goals for percentage (goals for / [goals for + goals against]) over the past 3 seasons in zone start adjusted 5v5 situations as well as zone start adjusted 5v5 close situations and then compared the difference to the players teams record over the past three seasons.  The table below is what results.

Player Team GF% 5v5 GF% Close Close – 5v5 3yr Pts Avg. Pts
Doan Phoenix 50.3% 50.6% 0.3% 303 101.0
Chara Boston 50.7% 50.9% 0.2% 296 98.7
Toews Chicago 50.4% 50.6% 0.2% 310 103.3
Datsyuk Detroit 50.8% 51.0% 0.2% 308 102.7
Weber Nashville 50.5% 50.7% 0.2% 303 101.0
Backes St. Louis 50.8% 51.0% 0.2% 286 95.3
E. Staal Carolina 50.4% 50.5% 0.1% 253 84.3
Ribeiro Dallas 50.5% 50.6% 0.1% 272 90.7
Gaborik Ny Rangers 50.1% 50.2% 0.1% 289 96.3
Malkin Pittsburgh 50.1% 50.2% 0.1% 315 105.0
Ovechkin Washington 49.9% 50.0% 0.1% 320 106.7
Enstrom Winnipeg 50.1% 50.2% 0.1% 247 82.3
Weiss Florida 50.3% 50.3% 0.0% 243 81.0
Plekanec Montreal 50.4% 50.4% 0.0% 262 87.3
Tavares NY Islanders 50.3% 50.3% 0.0% 231 77.0
Hartnell Philadelphia 50.1% 50.1% 0.0% 297 99.0
J. Thornton San Jose 50.9% 50.9% 0.0% 314 104.7
Kessel Toronto 50.1% 50.1% 0.0% 239 79.7
H. Sedin Vancouver 50.0% 50.0% 0.0% 331 110.3
Nash Columbus 50.9% 50.8% -0.1% 225 75.0
J. Eberle Edmonton 50.6% 50.5% -0.1% 198 66.0
Kopitar Los Angeles 50.6% 50.5% -0.1% 294 98.0
M. Koivu Minnesota 50.7% 50.6% -0.1% 251 83.7
Parise New Jersey 50.8% 50.7% -0.1% 286 95.3
Getzlaf Anaheim 51.0% 50.8% -0.2% 268 89.3
Roy Buffalo 50.3% 50.1% -0.2% 285 95.0
Stastny Colorado 50.3% 50.1% -0.2% 251 83.7
Spezza Ottawa 50.6% 50.4% -0.2% 260 86.7
Stamkos Tampa 50.2% 50.0% -0.2% 267 89.0
Iginla Calgary 50.5% 50.2% -0.3% 274 91.3
50.4% 50.5% >0 97.3
50.3% 50.3% =0 91.3
50.6% 50.4% <0 86.6

The list above is sorted by the difference between the oppositions 5v5 close GF% and the oppositions 5v5 GF%.  The bottom three rows of the last column is what tells the story.  These show the average point totals of the teams for players whose opposition 5v5 close GF% was greater than, equal to and less than the opponents 5v5 GF%.  As you can see, the greater than group had a team average 97.3 points, the equal to group had a team average of 91.3 points and the less than group had a team average of 86.6 points.  This means that good teams have on average tougher 5v5 close opponents than straight 5v5 opponents and weak teams have tougher 5v5 opponents than 5v5 close opponents which is exactly what we predicted.  It is also not unexpected.  Weak teams tend to play close games against similarly weak teams while strong teams play close games against similarly strong teams.

Another important observation is how little deviation from 50% there is in each players opposition GF% metrics.  The range for the above players is from 49.9% to 51.0%.  That is an incredible tight range and reconfirms to me the small importance QoC has an a players performance, especially when considering longer periods of time.

I also conducted the same study using fenwick for percentage as the QoC metric instead of goals for percentage but the results were less conclusive.  The >0 group had an average of 93.2 team points int he standings, the =0 group had 93.4 team points in the standings and the <0 group had 83.25 team points in the standings.  Furthermore there was even less variance in opposition FF% than GF% and only 12 teams had any difference between opposition 5v5 and opposition 5v5 close FF%.  For me, this is further evidence that fenwick/corsi are not optimal measures of player value.

Finally, I looked at the difference in player performance during 5v5 situations and found no trends among the different performance levels.  For GF% almost every player had their 5v5 close GF% within 4% of their of their 5v5 GF% (r^2 between the two was 0.7346) and for FF% every player but Parise had their 5v5 close FF% within 1.7% of their 5v5 GF% (r^2 = 0.945).  Furthermore, there was consistency as to which players saw an improvement (or decrease) in their 5v5 close GF% or FF% so it seems it might be luck driven (particularly for GF%) or maybe coaching factors.

So what does this all mean?  It means that in 5v5 close situations good teams have a bias towards tougher QoC than weak teams do.  Does it have a significant factor on player performance?  No, because the QoC metrics vary very little across players or from situation to situation (from my perspective QoC can be ignored the majority of the time).  Does it mean that we should be using 5v5 close in our player analysis?  I am still not sure.  I think the benefits of doing so are still probably quite small if there is any at all as 5v5 close performance metrics mirror 5v5 performance metrics quite well and in the case of goal metrics using the larger sample size of 5v5 data almost certainly supersedes any benefits of using 5v5 close data.

 

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