Mar 022012
 

A lot has been made about Joffrey Lupul’s “career year” this year and some Leaf fans are even suggesting that now is the time to trade him while his value is at an all-time high.  While it is true that he is on pace for career high in goals and points I would like to suggest that this is not because he is having a ‘career year’ but that he is being given greater opportunity.  He has always been this good and there is no reason to expect that he cannot repeat this years performance next season.

When I analyze a player I like to look at “on-ice” stats because I believe a player can contribute to a teams success without generating individual goals and assists.  But, since on-ice stats are teammate dependent I like to look at how his teammates do with and without the player on the ice with him.  So, let’s look at some of Lupul’s linemates 5v5 close faceoff adjusted goals for per 20 minutes with and without Lupul over the past 5 seasons.

Year Teammate Together TM w/o Lupul % Inc w/ Lupul
2011-12 Kessel 1.418 0.789 79.7%
2011-12 Bozak 1.068 1.268 -15.8%
2010-11 Bozak 0.979 0.718 36.4%
2010-11 Kessel 0.989 0.769 28.6%
2008-09 Hartnell 1.61 0.659 144.3%
2008-09 J. Carter 1.627 0.73 122.9%
2007-08 M. Richards 1.718 0.683 151.5%
2007-08 Umberger 1.915 0.631 203.5%
2007-08 Briere 1.061 0.536 97.9%

The above table includes all players Lupul has played 100 minutes of 5v5 close ice time with over the past 5 seasons including their GF20 together and Lupul’s teammates GF20 when not playing with Lupul.  The final column is how much better the teammates GF20 is playing with Lupul compared to without Lupul.  As you can see, in every single season Lupul has made his linemates significantly better offensively.  This is a good thing.

So, why are Lupul’s individual offensive numbers so much better this year?  A lot of it has to do with greater opportunity and the most important factor in opportunity is ice time.   Let’s take a look at Lupul’s even strength goal production over the past 5 seasons and compare it to his even strength ice time.

Year ES TOI ES G Min. bt goals
2011-12 984:59 17 57.9
2010-11 688:23 10 68.8
2009-10 299:05 10 29.9
2008-09 1039:42 19 54.7
2007-08 744:47 13 57.3

The “Min. bt goals” column is the average number of minutes that he spent on the ice at even strength between his even strength goals.  As you can see, this season is pretty much on par with what he has done in the past.

Another interesting thing to look at is his on-ice shooting percentage in 5v5 close zone start adjusted situations.  Over the past 5 seasons, starting with 2007-08, they are 14.04%, 12.05%, 9.09%, 11.64%, and 13.73%.  These are exceptional numbers, and among the best in the league.  I know not everyone believes in shooting percentages but I believe they are an integral component of producing offense.  As a result, a corsi-based analysis of Lupul will fail to show his true offensive value.

So, in conclusion, Lupul’s offensive production this season is not an anomaly, it is his ice time that is the anomaly.  He has almost as much even strength ice time this year than he has ever had and he has capitalized on it at more or less the same rate as he has in the past.  He is on pace for 32 goals this season and there is no reason to believe that he can’t be a 30 goal scorer next year as well.  The Leafs shouldn’t be considering trading Lupul this summer but rather they should be re-signing him to a long-term deal before his value really sky rockets in 2013 after putting up back to back 30+ goal, 70+ point seasons.

 

  13 Responses to “Lupul’s always been this good.”

  1.  

    Your analysis is interesting, though your numbers for ‘% Inc w/ Lupul’ do not make sense. You actually need to subtract 100 from each number. This will give Bozak a negative number, which makes sense since his production with Lupul is actually worse.

    The equation should be (‘Together’ – ‘TM w/o Lupul’)/’TM w/o Lupul’ * 100

    I’m not sure how you got the extra 100 points.

  2.  

    Great analysis as usual ( you were bang on J.Johnson!).Just curious how you are able to separate the numbers and calculate “together”/”apart”? I would like to do similar exercise with the Canucks but can’t find this data? Can it be done from your stat sight?

    thanks Dan

    •  

      I have written a program to calculate all the data from the box scores, shift tables, and play by play sheets found on NHL.com. You can find all the with/without data at stats.hockeyanalysis.com. Click on players, find the player you want and click on their name and that will take you to the individual players data. http://stats.hockeyanalysis.com/showplayer.php?pid=326 is Joffrey Lupul’s. At the top of that sheet is a table with a bunch of links to with/without data. Simply click on the year/years you want for 5v5, 5v5 faceoff adjusted or 5v5close faceoff adjusted ice time for goals or corsi data and you will see everything you need. Also included is performance vs opponents.

  3.  

    Wow! thanks..off to prove that no team can win the cup with
    A. Rome playing 14 minutes a night:)
    Thanks.

  4.  

    I noticed you used Goals F /A over a few seasons for your
    post on J.Johnson. Did you adjust for zones? And, do you feel
    Corsi and score effects are not useful in looking a defenders?

    thanks

    •  

      I did adjust for zone starts by ignoring the first 10 seconds after an offensive/defensive zone start. See http://hockeyanalysis.com/2012/01/23/adjusting-for-zone-starts/ for details.

      Score effects are real which is why I used 5v5 close data (zone start adjusted). You could 5v5 zone start adjusted as well because for the majority of players score effects probably aren’t that significant.

      I am not a fan of corsi because I think it ignores a major part of the game, shooting percentage. Yes, over small sample sizes corsi might be better because of the small sample size issues with shooting percentage and goal data, but if you have a full season of data or more, goal data is a much better predictor of talent. If you go back in the archives you can find more details as to why I believe this but if you want a quick and dirty rationale, take a look at a list of forwards sorted by their 4 year shooting percentages and you’ll notice the elite offensive players rise to the top. For example: http://stats.hockeyanalysis.com/ratings.php?disp=1&db=200711&sit=5v5close_f10&pos=forwards&minutes=2000&type=goal&sort=ShPct&sortdir=DESC

      Now compare that list to the fenwick for per 20 minutes rate and tell me which is more representative of the elite offensive talent in the NHL. http://stats.hockeyanalysis.com/ratings.php?disp=1&db=200711&sit=5v5close_f10&pos=forwards&minutes=2000&type=fenwick&sort=F20&sortdir=DESC

      •  

        Thanks. I’m on your side, I’ve read your posts in depth and I agree with your point regarding the problems with Corsi and evaluating OFFENSIVE talent. Its clear that top talent can maintain higher levels of Shooting %,. I live in Vancouver and watch the Canucks and they are a perfect example of a team that keeps producing High shooting % over seasons.

        Sorry, I wasn’t clear.
        I’m trying to focus on the defensive part of the game.
        I am looking for the best way stat/formulas that help us establish top defensive players, teams (apart from goaltending)I was curious
        what your approach is to evaluating the defensive strength of teams?

  5.  

    Or,stated more precisely,

    Do you think that defenders and teams(systems) (apart from goaltending)
    can “decrease shooting %” in the same way top offensive players can?

    •  

      I think the most difficult question to answer in hockey analytics is how do we separate a goaltender performance from the team/players in front of them and vice versa. I do believe that players can decrease opposition shooting percentage but to determine if they can we need to determine what a goaltenders expected save percentage would be if he had an average defensive team in front of him but to do that we’d need to know how good a defensive team he is playing behind which we don’t know.

      But, when I see Brian Elliot and Mike Smith performing so well in St. Louis and Phoenix but stunk so badly in Ottawa and Tampa one year ago it is difficult not to believe defensive ability of the team in front of them is a factor (both Phoenix and St. Louis play a defensive system and neither have elite offensive players). Conversely, look at how bad Bryzgalov is doing in Philadelphia compared to what he did in Phoenix. Philadelphia plays a more offensive oriented game. Is it complete coincidence? I suspect not.

      I took a stab at answering the question about a defenders effect on save percentage in this post: http://hockeyanalysis.com/2012/02/09/defenders-effect-on-save/ I did it by looking at a players on-ice save percentage and compared it to his goaltenders save percentage when they are not on the ice with the player. This only really gives us an idea of a players defensive ability relative to his teammates, but it is a start.

  6.  

    Hi there. Just found the site and I’m impressed. I love crunching numbers and hockey is a pretty challenging area to do it in.

    I think the only way to really get solids stats for hockey is to utilize visual analysis of the games. Something like:
    http://www.sciencedirect.com/science/article/pii/S0262885606003635
    What are your thoughts? I just don’t see any other way to be able to deal with all the confounding factors involved in the game.

    In my opinion the best way to compare players is to be able to visually identify situations and evaluate the outcome of a player’s decision and action. For example, if a player has the puck in a spot on the ice and has the option of two passes or a shot, how successful is he in making those passes compared to the rest of the league? Or in scoring on that shot? It’d also be useful in determining what the highest % decision is. For goalies you could examine SV% or Rebound% in relation so shot speed, height, angle, and location (and also the position of the goalie of course).

    Anyway the technology is still developing so I really like the approaches you’ve taken here.

  7.  

    “….I did adjust for zone starts by ignoring the first 10 seconds after an offensive/defensive zone start. See http://hockeyanalysis.com/2012/01/23/adjusting-for-zone-starts/ for details….”

    I like this idea to adjust. I notice you have *zone adjusted on your stat site as an option. How is this ‘old’ adjustment done? Do you think it has value? I don’t think its the ‘new’ f10 is it?
    thanks

    •  

      It is the new ‘f10′. It’s the only zone start adjustment I have used. I have thought about other methodologies but figured this ‘f10′ method was just too simple and straight forward and effective to spend much time exploring any other adjustment methods.

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