On-ice Shooting Percentage as a Talent

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Oct 102011
 

There has been an interesting discussion of on-ice shooting percentage at Tyler Dellow’s mc79hockey.com.  I have argued that we need to look at on-ice shooting percentage as a talent, and not something that just happens randomly while others have largely dismissed it.  One person in particular is Gabe Desjardin’s who has a followup post on his blog largely dismissing its importance.

In his blog post Gabe first discusses Gaborik’s value just considering his on-ice shooting percentage.

So are these totals 75% skill then?  Let’s do a quick check on how many goals that skill would be worth: 1000 on-ice shots/season * 2.5% above mean * 75% = 18.75 goals above average.  Double that to get to an approximate replacement level of 37.5 goals or just over six wins.  The current price for one win on the free agent market is roughly $3M, so we’d estimate Gaborik’s offensive value at more than $18M.

Gabe doesn’t believe any one player could be worth $18M based just on shooting percentage so he tries to shoot a hole in that by looking at 2yr vs 2yr regression.

Needless to say, anytime you come up with a metric that says a player should get paid $18M, you have to go back and check your math.  I did that by splitting the last four years into two two year periods (2007-08/2008-09 vs 2009-10/2010-11) and comparing on-ice shooting percentage among players who had 1000+ on-ice shots in each period.  I found that player on-ice shooting regressed 80% to the mean from the first set to the second, which puts Gaborik’s apparent talent closer to $5M.

Ok, so we have Gaborik’s value down to $5M.  That still seems pretty large to me but Gabe dismisses that further by suggesting some of that $5M has to be attributed to his linemates, arena bias, and strangely, a players opponents (particularly at home).

This is a key point, of course, and one that may not come through when we talk about team-level effects or try to figure out the value of individual top six forwards: when a #1 line plays against a #4 line, their shooting percentage goes up relative to when they’re playing power-vs-power.

Here is the thing.  The whole reason I participate in these debates is to suggest that players do in fact have the ability to drive or suppress shooting percentage and thus we must consider shooting percentage, in addition to corsi, when evaluating players.  So, it amazes me when I am debating someone that is trying to minimize the ability to drive/suppress shooting percentage that they bring up such observations that a players shooting percentage will go up when they are playing weaker players (i.e. the fourth line) who I presume can’t suppress shooting percentage as well as the stronger players.   There is clearly something wrong with the logic there.  Players don’t have the ability to suppress shooting percentage, but fourth liners are worse at suppressing shooting percentage than first liners??

The other thing I want to discuss is Gabe’s calculation that shooting percentage regresses 80% to the mean based on his 2yr vs 2yr calculation.  I won’t dispute his math because it is probably true, but I will suggest that I don’t believe that a league-wide observation can be applied to individual players.  There are a number of factors that influence a players on-ice shooting percentage.

1.  The quality of his linemates.

2.  The quality of opposition.

3.  Style of play (i.e.  aggressive offensive game vs defensive style).

4.  Score effects

Team building generally revolves around a small number of players.  Pittsburgh has Crosby, Malkin and Staal as their core forwards, everyone else is pretty much interchangeable.  Pretty much every team is like this.  A lot of those interchangeable parts move from team to team or even line to line on the same team or get asked to play different roles on the same team.  An injury to Kunitz and Pascal Dupuis gets bumped from the third line to playing with Crosby.  For these mostly interchangeable parts there can be a lot of variation in who they play with, the team they play on, and who they play against, and the roles they are asked to play.  All these factors are at play when Gabe calculates his 80% regression to the mean.  The good players who have well-defined roles don’t see near the same variation in their on-ice shooting percentages.  Look at the Crosby’s and Gaborik’s.  They are consistently at the top of the list.  Look at the Moen’s and Marchant’s and Pahlsson’s, they are consistently near the bottom of the list.  The players we perceive as good offensive players are at the top of the list.  The players we perceive as weak offensive players, or defensive minded players, are at the bottom of the list.  That’s a talent that we must consider.

 

 

Oct 052011
 

The Leafs traded for David Steckel yesterday and while this is by no means a significant trade my first reaction to it was a very positive one.  A fourth round pick is almost worthless and Steckel is a more than useful quality defensive third/fourth line guy who can kill penalties, something the Leafs desperately need.  Upon further review of the stats, I still like the trade because of it’s low risk but my thoughts on Steckel are a little more mixed than I first believed.

The Good

On the surface, Steckel looks like a premiere defensive forward.  Over the past 4 years, Steckel has the 9thth lowest on ice goals against per 20 minutes of the 250 forwards with 1500 5v5close minutes of ice time and he has been consistently very good at keeping the puck out of his own net at even strength.  His four year HARD+ rating is 1.152 and his HARD+ ratings for the past 4 seasons are 1.112, 1.262, 1,102 and 1.094.  All of these things point to Steckel being a good, or maybe very good, defensive forward.

The Bad

Throwing a damper on everything I just said, his quality of competition is quite weak.  His OppGF20 (opposition goals for per 20 minutes) ranks 227th of 250 and surprisingly he has over the past 4 seasons had slightly more offensive zone starts than defensive zone starts.  Now this isn’t all bad.  His opponents on average scored at a rate of 0.766 goals per 20 minutes of 5v5close ice time while Steckel and his teammates held them to 0.499 goals per 20 minutes but I would have more confidence in his defensive numbers if he was playing against top level opponents.

The Ugly

One of the key roles the Leafs likely acquired Steckel for is to provide some desperately needed help to their woeful penalty kill.  The problem is, Steckel’s PK numbers are quite woeful as well.  Of the 63 forwards with 500 4v5 PK minutes over the past 4 seasons, Steckel ranks in 48th in goals against per 20 minutes though he is a much better, but still average, 28th in fenwick against per 20 minutes.  Furthermore, the quality of his opponents on the PK hasn’t been all that great either as he ranked 57th of 63 in OppGF20 and 60th of 63 in OppFenF20.  Add it all up and it is quite likely that Steckel has been a below (maybe well below) average PK guy over the past four seasons.  That isn’t good news for the Leafs PK in 2011-12.

The Skinny

Although the numbers cast some doubts as to whether Steckel will live up to my initial reaction when I heard the trade, I still like the trade because it is a low risk trade and adds some defensive minded depth and size to the Leafs lineup.  I’ll take a wait and see attitude with regards to Steckel being a quality addition to the Leafs penalty kill unit but at the very least he’ll be a quality addition to the fourth line.  A fourth line that includes Steckel along side Mike Brown and Colton Orr could at the very least be a physically intimidating energy line that hopefully is more than responsible defensively and that isn’t all bad.

 

Sep 272011
 

Yesterday I posted my 100% statistics based predictions for the eastern conference, today here are the predictions for the western conference.  See the eastern conference predictions for more details on how these predictions are calculated but generally speaking I use my player rating system and combine them with my estimates for ice time for every player to come up with a predicted goals for and against for every team.  I haven’t converted the goal differentials to won-loss records because I actually think looking at the predicted goals for and against and goal differential provides better insight into the strengths and weaknesses of each team.

Predicted Last Season
Team GF GA GF-GA GF GA GF-GA
Chicago 235.6 205.0 30.6 252 220 32
Vancouver 238.7 213.0 25.8 258 180 78
San Jose 228.3 208.2 20.1 243 208 35
St. Louis 233.3 217.9 15.4 236 228 8
Calgary 234.1 223.2 11.0 241 230 11
Detroit 241.7 233.9 7.8 257 237 20
Los Angeles 216.6 211.5 5.0 209 196 13
Nashville 214.0 210.3 3.7 213 190 23
Anaheim 237.2 234.7 2.5 235 233 2
Dallas 221.7 221.6 0.1 222 226 -4
Phoenix 210.5 217.9 -7.4 226 220 6
Minnesota 210.7 230.1 -19.4 203 228 -25
Columbus 216.9 239.4 -22.5 210 250 -40
Colorado 205.4 239.5 -34.1 221 287 -66
Edmonton 204.9 252.0 -47.1 191 260 -69

As I mentioned in the eastern conference predictions, while I think the above standings seem for the most part reasonable I think there will be more spread in the goals for column.  The top offensive teams will probably end up scoring 20+ goals more than is predicted above.  Last season Vancouver had 258 goals to lead the conference and that was a low total for a conference leader.  The prior 2 seasons the leader had 268 and 289 goals scored.

As far as surprises go, seeing St. Louis fourth and Calgary fifth were definitely surprises but then Calgary’s goal differential is predicted to be the same as last season and the Blues goal differential only rises moderately from +8 to +15.4 based mostly by reducing the goals against.  These teams weren’t that far from making the playoffs either so while a little surprising on the surface, might not be all that unreasonable of a prediction.  Los Angeles being predicted to score only 5 more goals than they give up is a surprise too.

Team GF Team GA
Detroit 241.7 Chicago 205.0
Vancouver 238.7 San Jose 208.2
Anaheim 237.2 Nashville 210.3
Chicago 235.6 Los Angeles 211.5
Calgary 234.1 Vancouver 213.0
St. Louis 233.3 St. Louis 217.9
San Jose 228.3 Phoenix 217.9
Dallas 221.7 Dallas 221.6
Columbus 216.9 Calgary 223.2
Los Angeles 216.6 Minnesota 230.1
Nashville 214.0 Detroit 233.9
Minnesota 210.7 Anaheim 234.7
Phoenix 210.5 Columbus 239.4
Colorado 205.4 Colorado 239.5
Edmonton 204.9 Edmonton 252.0

It looks like it could be another tough year for fans in Edmonton and Colorado as they are predicted to be the bottom 2 teams in goals scored as well as be the bottom 2 teams in goals allowed.  I am sure the fans in Washington are smiling since they have Colorado’s first round pick which they acquired in the Varlamov trade.  Based on the predictions above, I’d say there is a more than decent chance it is a top 5 pick overall.

The final interesting thing is that these predictions predict the eastern conference to have a better goal differential than the western conference.  This is a change from recent seasons when the west has generally been the better conference.  Not sure if this will become reality or not but it is worth watching.  There were a number of quality players that moved from the west to the east this summer (Brad Richards, Ilya Bryzgalov, Brian Campbell, Christian Ehrhoff, Robyn Regehr, Tomas Fleishmann, Scottie Upshall, Steve Sullivan, Matthew Lombardi, Joel Ward, etc.) which probably weren’t fully offset by the players going west (Carter, Richards, Wisniewski, etc.).  Whether the shift is enough to make the east as good or better than the west we’ll have to wait and see.

 

Sep 262011
 

A week or two ago I presented a prediction of the eastern conference using a purely statistics based analysis.  There were a number of limitations with the process which I outlined at the beginning of the post but I have fixed some of those so this is version 2.0 of the prediction algorithm.  Let me summarize the process.

  1.  I took each teams current rosters and estimated the amount of even strength, power play and shorthanded ice time each player on the roster would play.  For veteran players, the estimates were loosely based on previous years ice time which should give us a pretty accurate number for the majority of the players, serious injuries aside.
  2. I then combined the ice time data with my 3-year 5v5close, 5v4 power play and 4v5 shorthanded HARO+ and HARD+ ratings.  I used 3-year ratings because I think they more reliably reflect each players true abilities where as one year, and even two year, ratings have significant margins of error associated with them.
  3. For rookies and other relatively un-established players I had to take guestimates at their ratings and their ice times.  Most rookies or players with little NHL experience to develop ratings with I guestimated them to be below average players, except for players who are premiere prospects in which case I rated them more like an average player.   It is actually somewhat rare for rookies to perform significantly above average, especially defensively.
  4. Unlike my previous ratings, I did make adjustments for strength of schedule.
  5. Also, unlike my previous ratings, I did make adjustments for teams that might get more or less than an average number of power play or penalty kill opportunities.  To do this I used each teams total power play and short handed situations over the past 2 seasons and compared them to the league average.  For teams which more powerplays than the average team had their power play goal production increased and those with less had their power play goal production decreased accordingly.  The same was done for the penalty kill.  Of course, if a team changes their playing style to take or draw more or fewer penalties than in the previous 2 seasons the reliability of the predictions will be degraded somewhat.

As with the previous post, I haven’t converted goals for/against into points in the standings but this gives you an indication of how the numbers seem to view the teams talent levels.  So, with that said, here are your eastern conference predictions.

Predicted Last Season
Team GF GA GF-GA GF GA GF-GA
Boston 227.1 203.5 23.5 244 189 55
Pittsburgh 242.0 219.9 22.1 228 196 32
Buffalo 235.4 217.7 17.6 240 228 12
Washington 236.3 218.9 17.3 219 191 28
Philadelphia 239.3 222.1 17.2 256 216 40
Toronto 245.3 235.6 9.6 213 245 -32
Tampa Bay 233.6 224.4 9.3 241 234 7
NY Rangers 217.5 210.8 6.8 224 195 29
Montreal 226.5 225.6 0.9 213 206 7
Carolina 227.5 231.0 -3.5 231 234 -3
Florida 211.4 216.6 -5.2 191 222 -31
New Jersey 202.3 210.1 -7.8 171 207 -36
NY Islanders 227.4 240.5 -13.1 225 258 -33
Winnipeg 210.3 235.5 -25.2 218 262 -44
Ottawa 189.5 251.8 -62.3 190 245 -55

Before getting into some team specific observations, a first observation worth noting is that the goals for and against predictions seem to be more compressed than what typically occurs in the NHL standings.  The predicted goals for totals range from a high of 245 to a low of 189.  The low of 189 is perfectly reasonable (the lows from the previous 3 seasons are 171, 196 and 190) but the high of 245 is well below the high totals of previous years.  Last season the Canucks scored a high of 258 goals, the previous season the Capitals led with 313 followed by the Canucks with 268 and in 2008-09 the Red Wings led with 289 goals.  I am not sure if this is evidence of increased parity or whether it is a flaw within the ratings system and/or the prediction algorithm.

Team GF Team GA
Toronto 245.3 Boston 203.5
Pittsburgh 242.0 New Jersey 210.1
Philadelphia 239.3 NY Rangers 210.8
Washington 236.3 Florida 216.6
Buffalo 235.4 Buffalo 217.7
Tampa Bay 233.6 Washington 218.9
Carolina 227.5 Pittsburgh 219.9
NY Islanders 227.4 Philadelphia 222.1
Boston 227.1 Tampa Bay 224.4
Montreal 226.5 Montreal 225.6
NY Rangers 217.5 Carolina 231.0
Florida 211.4 Winnipeg 235.5
Winnipeg 210.3 Toronto 235.6
New Jersey 202.3 NY Islanders 240.5
Ottawa 189.5 Ottawa 251.8

 

The teams with the largest predicted improvements in goal differential are the Leafs (42 points), the Devils (28), Panthers (26), Islanders (20), and Jets (19) while the teams predicted to fall back the most in terms of goal differential are Boston (-31), Philadelphia (-23) and the Rangers (-22).   The predicted top 6 scoring teams in the east are Toronto, Pittsburgh, Philadelphia, Washington, Buffalo and Tampa while the lowest scoring teams are predicted to be Ottawa, New Jersey, Winnipeg and Florida.  The teams with the predicted worst defense are Ottawa, Islanders, Toronto, Winnipeg and Carolina and the predicted best defensive teams are Boston, New Jersey, NY Rangers, Florida and Buffalo.  While there are a couple of surprises in there, most of those seem quite reasonable.  Now for some team specific observations.

Washington Capitals – The Capitals played a different game last season from the previous two seasons.  In 2008-09 they scored 268 goals but gave up 240, in 2009-10 they scored 313 and gave up 227.  Last season they improved significantly defensively giving up just 191 goals but their offense also suffered as they scored just 219.  The predictions are predicting the offense will come back next season but will cost them a little defensively.  Mathematically speaking it makes sense, but in reality it is difficult to say whether they will change their playing style back to a more offensive game or not at the cost of defense.  We’ll have to wait and see.

Toronto Maple Leafs – One of the biggest surprises in these predictions is the offense of the Maple Leafs.  They are predicted to score the most goals of any team, eastern or western conference.  A big reason for this is both Joffrey Lupul (who played just 28 games with the Leafs) and Tim Connolly have very good HARO+ ratings as do many of the returning Leaf forwards including Kessel, Kulemin, Grabovski, and MacArthur.  Even projected third line players Armstrong and Bozak have solid HARO+ ratings.  If the ratings are true, scoring goals shouldn’t be a problem for the Leafs and in fact the late season surge last year was predominantly a result of increased goal production and not solely due to the play of James Reimer.  The Leafs problematic defensive ability is still an issue for the Leafs though.

New York Rangers – It is difficult to fathom how a team that added Brad Richards will see their goal production drop from 224 to about 217.  This is a little dumbfounding, but the Rangers did lose 16 goals from Frolov and Prospal and the algorithm is certainly not predicting another 21 goals from Brian Boyle (his previous career high was 4) so it is certainly possible that Richards won’t dramatically increase the Rangers offensive output.  We’ll see.

Philadelphia Flyers – Unless some of the younger players really step up their games it is difficult to see them being as good a team as the Flyers from last season.  They are predicted to score 16 fewer goals but give up 6 more (despite Bryzgalov).

New Jersey – The Devils will be a dramatically better team this year, but they still may not be a very good one.  They have some highly talented forwards (Parise, Zajac, Kovalchuk) but they depth is weak and they will produce very little offense from the back end and who knows what Brodeur has left in the tank.

Florida Panthers – They brought a lot of players in this past off season and they should have an improved team but like the Devils it might be a stretch for them to make the playoffs.

Tomorrow we’ll take a look at the predictions for the western conference standings.

 

Mike Weaver – Premiere Defensive Defenseman

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Sep 152011
 

Over on Pension Plan Puppets there was a brief discussion of some of the top defensive defensemen and I suggested that Mike Weaver has to be considered among the top few defenders in the NHL.  The response was generally along the lines of ‘Mike who?’ and then followed with “he only looks good because he plays in front of Vokoun who may be the best goalie in the NHL.”  My thoughts on Vokoun being over rated aside, the numbers really do support Weaver as being a premiere level defensive defenseman.  Let’s look at some Mike Weaver numbers.

Over the past 4 seasons Mike Weaver played one season in Vancouver, 2 seasons in St. Louis and last season he was in Florida.  During that time there have been 173 defensemen who have played >1500 5v5 close (within 1 goal in first or second period or tied in third period) minutes and of those 173 defensemen Weaver ranks fourth in on ice goals against per 20 minutes of ice time.  He only trails Bryce Salvador, Sean O’Donnell and Paul Martin (3 other under rated defenders IMO).  Ranking 4th is a pretty good argument for why he is a great defender.  So what about the typical excuses for why he might rank so highly?

1.  Goalies make him look good.  Not really.  In the past 4 years he has played 2455:08 minutes of 5v5close ice time, 798:24 (32.5%) in front of Chris Mason, 587:45 (23.9%) in front of Vokoun, 390:58 (15.9%) in front of Luongo, 297:47 (12.1%) in front of Clemmensen, 185:04 (7.5%) in front of Conklin and some time in front of a few other lesser goalies.  At best you can argue he has played 45% of his time behind premiere level goalies (Vokoun and Luongo) with the remaining 55% behind second tier starters (Mason) or third tier starters and backups (Conklin, Clemmensen, etc).  In his year in Vancouver, the Canucks ranked a solid 7th in team goals against average but his 2 years in St. Louis the Blues ranked 12th and 11th and last year the Panthers ranked 14th so while he hasn’t played on any bad defensive teams he hasn’t played on any elite defensive teams either.  It’s difficult to make the case he has had an unusually significant benefit from playing in front of elite goalies or on elite defensive teams.

2. He Plays Easy Minutes.  Not really.  Over the past 4 seasons he ranks 45th of 173 defensemen with 34.1% of his face offs in the defensive zone and last season he started 36.9% of the time in the defensive zone or 21st highest of 157 defensemen with 500 5v5close minutes.  Over the past 4 seasons his opposition goals for per 20 minutes ranks 31st of 173 defensemen so he is seemingly playing against quality offensive forwards.  Last season the forwards he played most against were Ovechkin, Backstrom, Knuble, St. Louis, and Stamkos so yeah, that’s pretty good competition.  Over the past 2 seasons only Chris Phillips and Jay Bouwmeester have played more time on the 4v5 penalty kill than Weaver.  He is trusted playing tough minutes against top competition so the easy minutes argument is not valid.

While we are at it, Mike Weaver is another example why I do not like corsi/fenwick stats.  While Weaver has the 4th best on-ice 5v5close goals against per 20 minutes, he ranks a far less impressive (though still a little above average) 47th in fenwick against per 20 minutes.  The main reason why Weaver is so good defensively is he suppresses shot quality really well.  He ranks 3rd in shooting percentage against (or save percentage) while he is on the ice and he has been consistently above average over the past 4 seasons (6th of 176 in 2007-08, 63rd of 147 in 2008-09, 13th of 154 in 2009-10 and 22nd of 157 in 2010-11).  Three of the past 4 seasons he has been a top 25 defenseman in terms of shooting percentage against and the fourth and worst season he was still in the top half.  Sorry, but there is no ‘regressing to the mean’ there.

Mike Weaver is a premiere, and vastly under rated and under paid ($900,000), defensive defenseman.

 

Predicting the Eastern Conference

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Sep 132011
 

I have spent a lot of time and effort putting together player ratings so I decided it was time to finally put them to good use and attempt to use them to predict results for the upcoming season.  This is my attempt at the eastern conference and time permitting I’ll tackle the western conference in the future.

To accomplish this goal I used my 3-year (2008-11) offensive (HARO+) and defensive (HARD+) ratings at 5v5close, 5v4 powerplay and 4v5 penalty kill situations and combined all of the ratings for all of the players on each team and then converted them back to goals to come up with a predicted goals for and goals against for each team.  In doing this I estimated the ice time of every player so first and second line players will have a greater weight than third and fourth line players as well as reserve players and I also estimated how I believe each team will use their players on the power play and penalty kill.  I did this largely on each players PP and PK ice time last season.

I have made a few assumptions in these predictions.  First, teams will not suffer significant injuries.  Generally speaking, I cannot predict injuries so I have to go with this assumption.  There are a few exceptions though.  For example, I predicted that Sidney Crosby would miss the start of the season and miss about 1/4 of the season. There were one or two other players (Matthew Lombardi comes to mind) that I did this for but none were of the talent level of Crosby so the effects on the results will not be dramatic.  In any event, any significant injuries that occur will have an impact on results.

The second assumption I made was how to rate rookies and second year players that may not have a lot of ice time and thus not have reliable ratings.  For rookies, for the most part I rated them as slightly below average but it varied a bit depending on whether they are a big time prospect or not.  That said, if a team has a rookie or two that has an exceptional season it could affect the accuracy of my predictions.  For second year players or players without a significant history to develop ratings from I manually adjusted their ratings if they seemed to be out of whack (i.e. I manually regressed their ratings to the mean).  Some got their ratings bumped up, some bumped down.  For the most part these guys are not going to be key players to a team so errors in their guestimates are not likely to have a significant impact on overall team predictions.

The final assumption I made was that all teams will spend an equal amount of time on the power play and on the penalty kill.  This does not happen in reality and I am sure some teams are more prone to taking penalties (and drawing penalties) than other teams but I haven’t spent any time to attempt to predict that so for now I haven’t factored it in at all.

Oh, just remembered another assumption so this is the final final assumption I want to mention.  I have not factored in quality of competition.  If a team plays in an easier or more difficult division than another team this will affect their results somewhat.

So with all that said, here are the eastern conference predictions for the 2011-12 season.

Predicted 2011-12 Actual 2010-11
Team GF GA GF-GA GF GA GF-GA
Washington Capitals 248.0 224.6 23.4 224 197 27
Boston Bruins 236.1 216.2 19.9 246 195 51
Pittsburgh Penguins 241.4 222.3 19.1 238 199 39
Buffalo Sabres 242.1 225.3 16.8 245 229 16
Philadelphia Flyers 242.7 226.7 16.0 259 223 36
Montreal Canadiens 229.2 225.1 4.1 216 209 7
Tampa Bay Lightning 230.7 231.6 -0.9 247 240 7
NY Rangers 219.4 223.0 -3.6 233 198 35
Toronto Maple Leafs 241.0 248.4 -7.4 218 251 -33
NJ Devils 214.2 225.4 -11.2 174 209 -35
Florida Panthers 216.2 227.5 -11.4 195 229 -34
NY Islanders 230.5 244.6 -14.1 229 264 -35
Carolina Hurricanes 220.4 242.1 -21.7 236 239 -3
Winnipeg Jets 212.7 244.4 -31.7 223 269 -46
Ottawa Senators 195.5 258.1 -62.6 192 250 -58

As you may have noticed, I haven’t predicted won-loss records, just goals for and against which correlates fairly well with won-loss records.  I have also included last years goals for and against for reference.  Generally speaking, the good teams are at the top and the bad teams are at the bottom.  If my predictions are reasonably accurate the Capitals, Bruins, Penguins, Sabres and Flyers look like they should make the playoffs fairly easily while the Hurricanes (a bit of a surprise maybe), Jets and Senators are likely on the outside looking in come playoff time.  That leaves Montreal, Tampa, NY Rangers, Maple Leafs, Devils, Panthers and maybe the Islanders fighting for the final 3 playoff spots.  Generally speaking, that makes sense to me.

Let’s take a look at this data in a slightly different way.  Lets look at who has the greatest improvement in goal differential (GF-GA) from last season to m predictions for this upcoming season.

 

2011-12 2010-11
Team GF-GA GF-GA Diff
Toronto Maple Leafs -7.4 -33 25.6
NJ Devils -11.2 -35 23.8
Florida Panthers -11.4 -34 22.6
NY Islanders -14.1 -35 20.9
Winnipeg Jets -31.7 -46 14.3
Buffalo Sabres 16.8 16 0.8
Montreal Canadiens 4.1 7 -2.9
Washington Capitals 23.4 27 -3.6
Ottawa Senators -62.6 -58 -4.6
Tampa Bay Lightning -0.9 7 -7.9
Carolina Hurricanes -21.7 -3 -18.7
Pittsburgh Penguins 19.1 39 -19.9
Philadelphia Flyers 16.0 36 -20.0
Boston Bruins 19.9 51 -31.1
NY Rangers -3.6 35 -38.6

Generally speaking the teams that have the highest predicted improvement were teams that had poor seasons last year and the teams with the greatest predicted fall back are teams that had good years last year.  There is probably a regression to the mean happening here.  The good teams last year probably had some luck going their way and the teams at the bottom of the standings probably had some bad luck.

For the gainers, the Devils potential gain is fully understandable.  They had a horrendous first half of last season but played much better in the second half.  They should be closer to their second half performance this upcoming year.  The Florida Panthers spent a lot of money on free agents and should have an improved team, but still may not make the playoffs.  The Maple Leafs, Islanders and Jets are probably more in the had some bad luck last season and will regress to the mean category though their young players should be a bit better too.

The Rangers predicted fall back is a bit of a surprise considering they signed Brad Richards but they lost Drury, Frolov, Gilroy, McCabe and Prospal.  Their projected defense looks potentially very weak.  After Staal and Girardi you have Sauer, McDonagh, Erixon, Del Zotto, and Eminger all of whom are very young with little or no experience or in the case of Eminger a one time quality prospect that never really established himself as an NHL regular.

The table below shows the predicted top offensive and defensive teams.

Team GF Team GA
Washington Capitals 248.0 Boston Bruins 216.2
Philadelphia Flyers 242.7 Pittsburgh Penguins 222.3
Buffalo Sabres 242.1 NY Rangers 223.0
Pittsburgh Penguins 241.4 Washington Capitals 224.6
Toronto Maple Leafs 241.0 Montreal Canadiens 225.1
Boston Bruins 236.1 Buffalo Sabres 225.3
Tampa Bay Lightning 230.7 NJ Devils 225.4
NY Islanders 230.5 Philadelphia Flyers 226.7
Montreal Canadiens 229.2 Florida Panthers 227.5
Carolina Hurricanes 220.4 Tampa Bay Lightning 231.6
NY Rangers 219.4 Carolina Hurricanes 242.1
Florida Panthers 216.2 Winnipeg Jets 244.4
NJ Devils 214.2 NY Islanders 244.6
Winnipeg Jets 212.7 Toronto Maple Leafs 248.4
Ottawa Senators 195.5 Ottawa Senators 258.1

It is probably not a surprise that the Capitals, Flyers, Sabres, Penguins, Bruins and Lightning are among the top offensive teams but it is interesting to see the Maple Leafs move up the offense list.   It is a common belief that the Leafs late season success last season was because of the play of goalie James Reimer and Reimer did play a part, but in reality, much of the reason for the success was actually due to the fact that the Leafs scored a lot of goals.  Add Connolly and Liles into the mix and the Leafs can put out three lines who can score so while they may not have the elite offensive players some of the other teams have, they have depth (not unlike the Bruins actually whose top point producer was Krejci with just 62 points – Kessel had 64 for the Leafs).  Defensively it seems the Leafs may continue to struggle.  They are not a good defensive team and they desperately need to figure out how to improve their penalty kill.  Defense could be a problem, even with improved goaltending (which may or may not be reality – Reimer had success over a somewhat small sample size and Gustavsson has never performed well).

It is probably worth saying a word or two about the Ottawa Senators.  It seems they will struggle to score and will struggle to keep the puck out of their own net.  The Senators may be in for a tough season but it will be a season of evaluation of young players and hopefully (for Sens fans) progress.  On any given night they will potentially have 6-8 rookies in the lineup.  Expect to see rookie forwards Bobby Butler, Mika Zibanejad, Erik Condra, Colin Greening, Zack Smith, Nikita Filatov and Stephane Da Costa in the line up through out the season as well as defensemen Jared Cowan, David Runblad, Patrick Wiercioch.  If some of these guys are truly ready to become solid NHL regulars they might not be as bad as the above tables suggest, but they will still likely be competing for the first overall draft pick (which is probably a good thing for them anyway)

Finally, let me suggest that you not all take these too seriously.  While I do think there is some merit to these predictions, if you think your team is ranked too low or another team is ranked too high, no need to have a fit over it.  I really don’t know how accurate they are and a lot can happen to alter what really happens anyway.  I wanted to post these in part to generate a discussion but also in part so I can track these predictions as the season progresses and come the end of the season look back see how well this unbiased, mostly mathematical prediction system performs.

 

Sep 062011
 

Here is my view of the top 15 goalies in the NHL today.  Although I haven’t presented a hard statistical argument for each goalie I did consult a variety of statistics when compiling the list including some advanced stats and my own player rating system.

1.  Tim Thomas – Even if you account for the fact that Thomas plays behind a great team with maybe the best shutdown defenseman in the league (Chara) his numbers are still incredibly impressive.  Easily the best goalie in the NHL right now.

2.  Pekka Rinne – I am not sure everyone knows how good this guy is.  He has a good defense in front of him, but he keeps his team in every game and that is important for a team with no real game breakers on offense.

3.  Roberto Luongo – Luongo has his critics and he is prone to have a slump from time to time, but overall he is one of the best in the game.

4.  Henrik Lundqvist – The NY Rangers have made a lot of mistakes with their forwards and defensemen but Lundqvist has been a stabilizing factor in goal and is probably the main reason the Rangers have competed for playoff spots the past few seasons.

5.  Jonathan Quick – His numbers may not appear to be quite as good as some of the goalies on the list below him but he doesn’t play behind a particularly defensive minded team and he plays in arguably the toughest division in hockey.

6.  Ryan Miller – The Sabres have lost a lot of players because they haven’t been able to afford their big salaries but they paid Miller and Miller is giving them a good return on their money.  Now that the Sabres have a billionaire owner who wants to win and will spend money to do so, it is time for Miller to really shine and take the Sabres deep into the playoffs.

7.  Jonas Hiller – I struggled where to put him because he has only had 177 games in the NHL and has never played 60 games in a season (might have last season if not for his injuries) but his performance in the games he has played has been consistently great.  Could move up a spot or two if he can put up equally impressive numbers over a 65+ game season.

8.  Ilya Bryzgalov – I struggled as to where to put Bryzgalov on this list.  He benefitted from a defensive minded style of play in Phoenix (especially since Tippett took over as coach) but he got the job behind a relatively weak Coyotes roster.  I’ll be interested to see how he performs over the next couple seasons behind a more talented offensive, but less defensive minded, Flyers team.

9.  Tomas Vokoun – I have been critical of Vokoun believing he isn’t an elite goalie and actually benefitted from playing in a weak division while others have argued he is one of the best in the NHL who hasn’t had success in terms of won-loss record purely because of the weak team in front of him.  Now he has one of the best teams in front of him (though still in a weak division) so we’ll see how things pan out for him.  He is a very good goalie, but not among the leagues best.

10.  Marc-Andre Fleury – He has won world junior championships, he has won a Stanley Cup and he has been on an Olympic gold medal team but he hasn’t quite been able to show the consistency needed to seriously be considered one of the true elite goalies in the NHL.

11.  Cam Ward – Ward has a history much the same as Fleury and he is a very good goalie, but like Fleury and Vokoun I am not ready to call him an elite goalie.

12. Martin Brodeur – Brodeur may deserve to be higher on the list but he had a poor season last year and has shown some decline in his game.  If he can have a bounce back year at age 39 he probably deserves to be higher on the list but if last season is the new norm for Brodeur he might actually deserve to be lower on the list.

13.  Nicklas Backstrom – There were some who have questioned Backstrom’s talent arguing he has benefitted from the Wild’s defense-first structure but the past couple of seasons saw the Wild convert to a more offensive game and last year he had a very good season once again.  He is a very good, reliable starting goalie in the NHL.

14.  Carey Price – Honestly, I don’t know where to put Price on this list.  His first few seasons in the NHL were mixed with inconsistent results but last season he had a great year.  I don’t believe that one season is enough to fairly evaluate players so I haven’t completely bought into last seasons success and this is why he is in the 14th spot.  If he can have a repeat performance in 2011-12 he should be ranked somewhere in the 5-8 slots.

15.  Antti Niemi – He has only played 102 NHL regular season games but has a Stanley Cup win and 40 playoff games under his belt.  He has had a bit of a roller coaster ride early in his career but his numbers are generally quite good.  If he can continue his development he should continue to move up this list.

Honorable Mention: Dwayne Roloson (now sure where to rank the soon to be 42 year old – he’ll start showing his age soon), Miikka Kiprusoff (still plays a ton of games, but results not up to snuff anymore) and Jaroslav Halak (needs more consistency and needs to play a 60+ game season).

What do you think?  Agree?  Disagree?  Did I miss anyone?

 

 

Sep 012011
 

I haven’t yet weighed in on the recent deaths of Derek Boogaard and Rick Rypien but with the passing yesterday of Wade Belak I have decided to let my thoughts be heard.

First off, it has been a sad summer with the passing of these three players and my sincere condolences go out to their families and friends.  Regardless of the circumstances, any death is a sad and somber event but even more sad and somber when the deaths are sudden and seemingly avoidable.

The above should be the number one thought on everybody’s mind today but clearly these three players are public figures who played a common “tough guy” role in the NHL and that has led some in the media and the general public to start to speculate as to whether there is a link between fighting in the NHL and these deaths.  While it is fair to suggest that the possibility of a link needs to be investigated and researched, we have to be extremely cautious to suggest that there is, or even likely is, a link.  The truth is, we don’t know.

Are the deaths of Boogaard, Rypien and Belak linked in any way to their roles as tough guys in the NHL?  We don’t know.

Do concussions (as a result of fighting) in some way  lead to depression?  We don’t know.

Are hockey players who suffer from depression more apt to become fighters as a way to feel accepted?  We don’t know.

“We don’t know” is the proper answer to all those questions (and many more) and we all should avoid making any speculative claims about anything at this point.  Only when we put the speculation and suggesting behind us and accept “We don’t know” can move to seeking serious and important answers to the questions above.  So with that, my request to the media is to stop the speculating and expressing of personal opinion and start to seek out serious answers.  Maybe I have missed it, but I have yet to hear or read about many (or any) interviews with experts in the field of concussions and/or depression and/or substance abuse in an attempt to connect the dots.  Doing so would truly move the discussion a step forward.

Update:  This post by Kent Wilson at  Houses of the Hockey blog is a worth while read and something I would consider “a step forward” in the discussion.

 

Aug 252011
 

A few weeks ago I questioned whether Luke Schenn was really a quality shut down defenseman as some believe and some people too exception to that.  Additionally, now that Lebda has been traded away the favourite defenseman whipping boy of Leaf fans seems to be Mike Komisarek.  Because of this, I decided we should conduct a comparison of the defensive ability of these two players to see if Leaf fans perceptions of these two players matches reality.

Schenn Komisarek
TOI 864:56 558:53
Goals Against per 20 min. 0.902 0.895
Opposition GF/20min. 0.767 0.757
HARD+ 0.810 0.840
Fenwick Against per 20min. 15.400 15.424
Opposition FenF/20min. 13.708 13.798
FenHARD+ 0.934 0.929
Def. Zone Face Off % 31.9% 37.8%

The above table shows all of the pertinent stats from the 2010-11 season for 5v5 close situations (close being teams are within 1 goal in first or second period or tied in third).  I have included both goal and fenwick based stats because I know some people prefer fenwick but in reality they tell pretty much the same story.

Last season when Luke Schenn was on the ice the Leafs gave up about the same number of goals against per 20 minutes (0.902 vs 0.895) and fenwick against per 20 minutes (15.400 vs 15.424) as when Komisarek was on the ice.  Schenn played against slightly tougher competition based on opposition goals for per 20 minutes while Komisarek played against slightly tougher competition based on opposition fenwick for per 20 minutes.  The end results were Komisarek had a slightly better HARD+ than Schenn (0.840 vs 0.810) but Schenn had a slightly better FenHARD+ (0.934 vs 0.929).  It should be noted that these ratings are quite poor for both players.

HARD+ and FenHARD+ take into account quality of teammates and competition, but they do not take into account zone starts.  For Komisarek, 37.8% of the faceoffs he was on the ice for were taken in the defensive zone while only 31.9% were in the defensive zone for Luke Schenn.  So, while all the other numbers are quite similar, the defensive zone face off percentage clearly means Komisarek faced tougher situations defensively than Luke Schenn.  I didn’t include the data above, but Schenn played with higher quality teammates than Komisarek (for example, Lebda’s #1 defense partner was Komisarek).

For interest sake, and to gain more confidence in the results, here are each players stats over the past 2 seasons.

Schenn Komisarek
TOI 1537:15 853:46
Goals Against per 20 min. 0.976 0.890
Opposition GF/20min. 0.751 0.759
HARD+ 0.783 0.853
Fenwick Against per 20min. 15.053 14.641
Opposition FenF/20min. 13.534 13.679
FenHARD+ 0.932 0.957
Def. Zone Face Off % 31.9% 35.7%

Over the past 2 seasons the edge is distinctly in Komisarek’s favour though in 2009-10 Komisarek had far fewer defensive zone faceoffs than last season (only 28.7%).  For the 2 years Schenn gave up more shots and goals per 20 minutes than Komisarek and faced weaker opponents (offensively at least) and had a much lower defensive zone face off percentage.

Based on the above, Leaf fans perceptions of Komisarek are pretty much true.  He has struggled defensively and hasn’t lived up to his contract or expectations but is also nothing to suggest that Luke Schenn has been any better at the defensive aspect of the game.  Schenn has just played more, not better.

Aug 212011
 

I have just updated my stats site (stats.hockeyanalysis.com) to include a number of new features.  The added features are:

1.  I have added a new situation – 5v5close.  5v5close is when the game is tied or within 1 goal in the first and second period or tied in the third period.  This is what I would call normal play where teams are more or less (depending on talent or game play/coaching style) equally interested in  playing offense or defense.  When teams get a larger lead or lead late in the game teams adjust their style of play to either protect that lead or go all out to score a goal to catch up.  It is probably better to use this than 5v5tied and maybe better than 5v5 (all 5v5 game score situations).

2.  I have included zone start data in the form of OZOF%, DZOF% and NZOF%.  OZOF% is the percentage of face offs taken in the offensive zone when the player is on the ice and DZOF% and NZOF% are the same for defensive zone and neutral zone faceoffs.  When we look at these by situation we can get an idea of how a players use gets changed by game score.  For example, last year Manny Malholtra had 38.8% of his 5v5 face offs in the defensive zone (29.1% offensive zone and 32.1% neutral zone) but when the Canucks were up by a goal his defensive zone faceoffs rose to 41.6% and when the Canucks were up by 2 goals they rose to 48.4%.

3.  I have once again put up with/against statistics for each player.  I had this data up a few years ago but when I re-designed my website I removed it but it is back.  Each player page (i.e. the Malhotra one linked to above) has a set of links at the top of the page to with/against statistics for each season (and multi-seasons) for 5v5 and 5v5 close situations for both goal and corsi data.  Each page shows how the player played with each teammate as well as how they played when they were not playing together as well as how the player performed against each opponent and how well the player and the opponent performed when not playing together.  These tables can give you an indication of which players are playing together and which players play well together as well as who a player plays against the most.  As an example, take a look at Manny Malhotra 5v5 goal with/against data for this past season and you will see he played the most with Raffi Torres (even more than with Roberto Luongo!) but it seems both players had better on ice results when apart.

4.  If you hadn’t noticed yet, a while back I added on ice shooting percentage (Sh%) and on ice opposition shooting percentage (OppSh%, subtract from to get on ice save %) which can be found with the goal data (but not with corsi, fenwick and shot data).

All totaled, there is well over 10 gigabytes of html, php and data base files of statistics (90% of which is in the with/against tables) so be warned, if you really wanted to you could spend days looking at it all.