Jump to content

E4 ... Ke2

Members
  • Content Count

    185
  • Joined

  • Last visited

Community Reputation

95 Excellent

About E4 ... Ke2

  • Rank
    Prospect

Profile Information

  • Gender
    Male
  • Location
    In the Bongcloud
  • Interests
    Sabres, Chess, Doctor Who (esp. missing episodes), mathematics, all sorts of science, engineering, and technology stuff

Recent Profile Visitors

The recent visitors block is disabled and is not being shown to other users.

  1. I love the Bee Gees. As I now have twice had a mod complain about my posts' length while I am trying to be thorough and clear with posts on statistics. I am just trying to be helpful, but it appears The Powers That Be would prefer that I not post anything that detailed. Far more importantly, as I was branded a terrorist in 1984 for reporting a threat on my person by an Indian national to the INS (precursor of ICE) and almost had him thrown out of the country, these posts were used as part of the basis of the claim that I am a trouble-maker (read: terrorist) by the Indian government to disallow me from going to my Dad's Alma Mater to donate the money he willed them (at the invitation of the Dean and my Dad's former roommate, former Prime Minister Manmohan Singh). I am very PISSED OFF that the Mods' comments against my posts were used to deny me that visit. As a Sikh, I want to go to Anmritsar and visit the 5 Takhats once more before I die. Your behaviour may have denied me that opportunity permanently. As I hope to do so in future, I shall do what The Man wants and excuse myself permanently. Before leaving, I will let you know what I bought when I was pulled over for DWB in Columbus, Ohio. Goodbye, everyone. It has largely been good.
  2. I actually think that the only time I trusted the way the Sabres were run under the Pegulas was when Lindy and Darcy were still here.
  3. As long as he passes the audition. https://www.youtube.com/watch?v=wboz-_KKVg4
  4. Actually, statistical analysis and such is part of why I find sports fun. And the analysis thereto made playing it and talking about it much simpler for me. So, believe it or not, this made it more fun and simpler for me. I will get to that when I finish working. I have been doing this as a stuff to do for a break.
  5. I put 4 stats in there. I avoided stats where I know we have far greater expertise on this board, such as TRpm and RApm. I will let the experts put them in. I added some evaluations of those statistics, because they are very old, and therefore have a long track record which we can evaluate. I would other people to chip in and let me know what they think and what adjustments to make. ASIDE: One assertion that gets a lot of support is @pi2000 claiming that because you can't rehearse set plays, only those that use concrete measures, such as TRpm, are worthwhile. I agree in principle that hockey is controlled chaos. So is the stock market. That does not make it impossible to derive figures based on seemingly ephemeral situations. It just means that you have to take them in context and with a bit of a jaded eye. For instance, I am a huge believe in quality of competition (Q of C) from a tactical evaluation; however as a long-term statistic, it is not worth nearly as much because almost all changes on the fly tend to flatten out the QofC. If we just ignored these numbers in other walks of life, virtually all applied mathematics that involves statistics and operations research would vanish. (You ever tried quantifying and modelling luck and trust?) Because of this uncertainty (kind of like a Hockey Heisenberg Uncertainty Principle), I don't use any single number to evaluate a player. For instance, I start with Adjusted Plus-Minus, incorporate Defencive Zone Starts, situational adjustments, and such to get as complete and nuanced an evaluation of a player who does not score a lot. (Or, for that matter determine if a big scorer should be moved because he scores a disproportionate amount of trivial goals in low-leverage situations and is completely defencively inept.)
  6. These are the fun stats, where we can measure greatness and incompetence. Many of these have obvious variations which I will not bother to enumerate here. Team Devastation Rating (good teams): Team Goal Differential / League Average Goal Differential Team Devastated Rating (bad teams): League Average Goal Differential / Team Goal Differential Individual Player Presence (think of this as "lone wolf" situations): rate of stat when player is on the ice / team's rate of stat without that player on the ice. So when you see that the Sabres' numbers all congregate in a tiny area near "BAD" on a chart with either Vladimir Sobotka or Tage Thompson, but they players' performance away from those two go all over the bloody map, you know they suck. Conversely, you get a very good idea of how much better the team has been with the addition of Jeff Skinner. You also find out how disastrous the Scandella-Ristolainen defence pair was. Without each-other on the ice, Scandella was at least a #6-8 D-man and Risto was in the #3-5 range. Put them together and you have, um, magic, I guess. Prorated Scoring seasons: Adjust goals and assists in all seasons to put all of the individual and team stats onto a uniform scale against the NHL historical average. Until the DPE, this was 1972-3. Now the historical average is 2006-7.
  7. Crucial Situations Originally Created: Early 1980s Creators: Roger Nielson, Al Arbour, Emile Francis, Jeff Z. Klein, Karl Eric-Reif, writers for the old hockey annuals Inspiration: Find out who the Joe Schlabotniks are who score goals in borderline irrelevant situations, make spectacular saves when the game is out of hand, etc. Logic: Track who is making key plays that preserve leads or tie games How to Measure It: What you measure and how you use it varied wildly from statistician to statistician. I will concentrate on tying or go-ahead goals, although you can do a lot more than this Examples: Cruicial scoring Goals and assists scored when tying the game or gaining the lead. Crucial +/- A player's plus-minus stats during crucial situations. Crucial Perseverance rating Which goaltenders are not allowing "the next goal." Adjustments and other examples: Who is put on the ice defencively in crucial situations Who is put on the ice offencively in crucial situations Who gets the puck out of the defencive zone after a crucial defencive zone start Who gets into the offencive zone when down 1 or tied. Who makes these plays in the 3rd period At the time of the creation of this stat, 70% of 3rd period leads were "safe". Performance in this part of the game was often called "critical" Defencive players who start a shift against a top offencive line. Goaltenders who replace injured goaltenders and do not have a back-up.
  8. Adjusted Plus-Minus Originally Created: Late 1970's Original Creators: Lou Nanne, Emile Francis, and others Inspiration: Try to get players on good teams and bad to be measured on the same relative two-way scale. Logic: How do we determine players on bad teams who are actually performing well, but are dragged down by lousy team-mates? Conversely, who on good teams is actually performing poorly because his team-mates inflate his raw numbers? How to compute it: There were actually 3 versions of this stat. Original Background: Easiest Used by Emile Francis and Lou Nanne to help evaluate player assignments, roles, etc. Allegedly pioneered in the 1950's (!) by Anatoli Tarasov, Arkady Chemyshev, Vsevolod Bobrov, Boris Kulagin, and Viktor Tikhonov -- even before the NHL adopted +/-. Computation Add up the raw +/- stats for a given team. Call that PM_total Divide PM_total by the number of skaters required to dress for a game. Call that PM_ave In the 1970's, when this was developed, that number was 16. Now, it is 18. For each player on the team who have played a "statistically significant" number of games, take his raw +/- and subtract the PM_ave. That is his original adjusted +/- Depending on whom you ask, this could be anywhere from 30 to 60. I personally say "half a season". First Revision Background: Some extra complexity Appeared in The Hockey News about 1980; introduced by Jeff Z. Klein and Karl-Eric Reif Was apparently used as far back as 1973 by Fred Shero and Joe Crozier Computation Each time a goal is scored on the ice, if the situation is one where you count a plus or minus, take the reciprocal of the number of players on the ice and multiply it by +1 (GF) or -1 (GA). This is PM_per_player for each player on the ice. Normal Simplification: Just assume 5 players on the ice, which is typical. Note that without this simplification, goals in 3-on-3 OT are over-valued somewhat. Add PM_per_player over the entire season for the entire team. This is the PM_ave. Also, for each goal where plus-minus applies, add the PM_per_player for each applicable player's adjusted plus-minus. This is PM_player_raw. For each player, subtract the PM_ave from PM_player_raw. This is his adjusted plus-minus. Second Revision Background: Add more situational understanding Pioneered by Viktor Tikhonov. Computation: Use any of those above. Adjustments: Do not include empty net goals. At the time, ENG outnumbered goals scored by the team that pulled the goaltender something like 30-1, so it inflated plus figures for defencive forwards and deflated plus figures for scoring players. Weight complete gaffes against specific players who screwed up, such as a defenceman coughing the puck up to an opposing forward in the slot and "crucial goals". Advantages: Fairly easy to derive from the raw data at the end of the year; easy to see when a player has played enough games to warrant this extra scrutiny; allows for underrated players to shine (e.g., Bill Hajt) and finds over-rated players relative to their peers (e.g., Ramsay-Luce-Gare were a better checking line than anything involving Bob Gainey!). Disadvantages: Still kind of crude; does not do as good a job finding good players on bad teams as it should (e.g., Ron Stackhouse); allows really good players to buoy the statistics of team-mates (e.g., Dallas Smith had the good fortune to be partnered with Bobby Orr and then Brad Park).
  9. Goaltender Perseverance ratings: (Save pct *6 + average shots against / game) / 0.6 Created: 1981 Creators: Hockey News Writers Jeff Z. Klein and Karl-Eric Reif Inspiration: Avoid using GAA for comparing goaltenders because good goaltenders on bad teams look worse than bad goaltenders on good teams. Logic: Save Percentage is generally a more predictable long-term, team-independent statistic than GAA. Add in the shots against per game to measure workload; thus the same save percentage for a goaltender on a weaker team that surrenders more shots will show a higher perseverance rating and therefore better performance. Advantages: First goaltender stat to try to rate goaltenders by combining personal performance and workload; found goaltenders who were over-rated by GAA who were terrible but played on very defencive teams. (Prototype: Pete Peeters later in his career) Disadvantages: Rated all shots equally; proportions were derived to rescale goaltenders to the THN staff's perceptions and evaluations. (Prototype: Tom Barrasso early in his career) Common Adjustments: Varying the dependence on the shot rates; incorporating shot difficulty; incorporating situational issues, such as a two-man advantage.
  10. Pending Admin approval, I created a new club so that we can have a repository for statistics references and discussion of the methodology, quality, etc. When I add a stat to the discussions, I will always give the simplest way to arrive at a variant of the stat, some more complex adjustments, etc. I will also try to add evaluation to the discussions. I have access to the very earliest of hockey data analysis, even pre-dating my own from 1992. I have the original computations used in The Hockey News for the columns, "For Argument's Sake." These are very primitive and date back to when a $2500 Atari 400 was close to top-of-the-line. For example Goaltender Perseverance ratings: (Save pct *6 + average shots against / game) / 0.6 Created: 1981 Inspiration: Avoid using GAA for comparing goaltenders because good goaltenders on bad teams look worse than bad goaltenders on good teams. Logic: Save Percentage is generally a more predictable long-term, team-independent statistic than GAA. Add in the shots against per game to measure workload; thus the same save percentage for a goaltender on a weaker team that surrenders more shots will show a higher perseverance rating and therefore better performance. Advantages: First goaltender stat to try to rate goaltenders by combining personal performance and workload; found goaltenders who were over-rated by GAA who were terrible but played on very defencive teams. (Prototype: Pete Peeters later in his career) Disadvantages: Rated all shots equally; proportions were derived to rescale goaltenders to the THN staff's perceptions and evaluations. (Prototype: Tom Barrasso early in his career) Common Adjustments: Varying the dependence on the shot rates; incorporating shot difficulty; incorporating situational issues, such as a two-man advantage. I did a pile of stuff when I got access to our old Sun machine at the MSU Math Department (the Solaris beta OS). I did a lot of work on what we call analytics back on my old Amiga in the 1990s. Most of this stuff has been largely superseded by modern analytics, but they are still pretty accurate, simple enough to compute, and easy enough to understand that I like to use them for basic analysis just to get a rough idea whenever I run into a claim that looks either counter-intuitive or completely out of whack.
  11. I can tell there are several posters with technical degrees. What I am saying is that I am the only one dumb enough to almost put the Riemann Mapping Theorem into an analytics post. As the living embodiment of someone with less common sense than the intersection of 4 main characters in "The Big Bang Theory", I can safely assert that what is "common sense" to everyone else on this board often requires somewhere between immense and inordinate reflection on my part.
  12. Once I get my laptop out, I will do something to make a reference for these graphs, stats, etc. IMHO, as the one most likely to come up with number crunching that requires a graduate math degree to interpret, I should do this. After watching the responses to some of the the analyses here, I think that explaining what I can see is like me trying to understand how Rashid Nezhmetdinov could think chess the way he did.
  13. One first line, two fourth lines, and a sixth. <sarcasm>See, we we had 4 top-6 lines.</sarcasm>
  14. This is so demonstrably false that I have to ask if you are like my wife, who never misses a second of action, but almost never watches the games critically. We had this discussion in early November, so we watched games in analysis mode until the All-Star Break so that she could see what I did. To disabuse you of your perception, I recommend that you start with the player on-ice charts which are available for each game of the season at nhl.com. Aggregates can be found at numerous raw and fancy stats sites. Thank you.
  15. Oh, heck. My point of logging in. I like the signing. Of course, anything that makes it more likely that Vladimir Putin^H^H^H^H^H Sobotka is not in the line-up was going to make me happy. Try this line-up out: Skinner-Eichel-Vesey Rodrigues-Mittlestadt-Reinhart Olofsson-Johansson-Asplund Girgensons-Larsson-Okposo Ruotsalainen-Smith-Nylander I protected Mittlestadt with a pair of 2-way players who can also play some Centre. I put a couple of young guys with Johansson to learn and allow both wingers to transition to Centre. I think the Eichel line will have great chemistry. I kept the 4th line together. I demoted Sheary based upon his play at the end of the season. The idea is to get 4 functioning lines and to clear out dead weight. I suddenly feel badly for Housley.
×
×
  • Create New...