Thursday, March 31, 2011
Saturday, February 5, 2011
Oversigning of College Athletes
The NCAA currently has a rule that states that an FBS program can have 85 scholarship athletes and can add 25 new scholarship athletes each Fall semester. Yet, some institutions have consistently oversigned the number of student athletes. Now I just found a website that is designed to document this issue of oversigning of student athletes called oversigning.com. Check it out, it well worth the look.
Friday, January 28, 2011
NHL Goalie Pay & Performance at the All-Star Break
In the Hockey News, there is a chart of each team's top two goalies pay and below the caption asks the following question: "If goaltending is the most important position in hockey, why do so many of the NHL's bottom-feeders slot up top with their blue ice budgets?".
That is a great question, so I thought that I would use the data on the total salaries each team's top two goalies and compare that with each team's performance, first to see if the idea is on target and to then try to explain these counter-intuitive results.
In order to figure this out, I collected each NHL team's standings data (Games Played (GP), Wins (W), Losses (L), OT, Goals For (GF), Goals Against (GA), Standings Points (PTS)) from nhl.com as of today (January 28th, 2011). Then I included the salary data from the Hockey News (January 31, 2011 edition p. 10) and ran a correlation between standings points and goalie salary. The correlation is -0.2777. Hence the writer is on target saying the teams with higher salaried top two goalies are negatively correlated with standings points at the 2010-2011 NHL All-Star break. As I tell my undergraduate students repeated, correlation does not imply causation. We have to work harder to draw conclusions (i.e. statistical inference) than just calculating the correlation between each NHL team's standings points and the combined salary of the top two NHL goalies on each team. To that I now turn.
In order to make a conclusion using statistical techniques, I decided to employ a linear regression on the data that I collected. The regression that I ran was standings points are a function or are determined by Goals For, Goals Against and Games Played. The result was that each variable is statistically significant (i.e. statistically is different from zero at least at a 95% confidence level) and each variable was of the expected sign. The estimated coefficient on Goals For and Games Played were positive (i.e. more goals for and more games played result in more standings points) and the estimated coefficient on Goals Against was negative (i. e. more goals against results in fewer standings points). Yet, when I then included goalie salary, the estimated coefficient was statistically insignificant (i. e. statistically likely to be zero or not relevant). So we can conclude that salary is not a factor in determining standings points up to the All-Star Break this year. Even if we adjust standings points for the number of games played, there still is no statistically significant relationship between team top two goalie salary and team performance.
OK, so there does not seem to be a statistical link between team top two goalie pay and team performance. What about goals against and salary? Shouldn't teams with higher goalie salaries have lower Goals Against? I would think so, but that is not what I find. I find that the estimated coefficient is negative and statistically insignificant - again meaning that there is no statistical relationship between Goals Against and NHL goalie payroll. Nor do I find a statistical relationship between Wins, Losses and NHL goalie payroll.
All of these findings are consistent with the paper Dave Berri and I published in the Journal of Sports Economics last year. We find that goalie salary and goalie performance are not statistically related. Why? Mainly because goalie performance is highly variable from period to period, game to game and season to season.
This is not a dig at NHL goalies. I think that it is really difficult to find an individual who can game after game consistently stop the puck from entering the net at the NHL level, because doing so against the level of NHL talent is so good, not because NHL goalies are not good. What makes people conclude the opposite is that they focus on those goalies (such as Martin Brodeur or Dominic Hasek) who for many years perform at an above average level, they they believe that all goalies can do so. If we look at Brodeur's stats this year, we see that he is (using our NHL goalie measure of Wins Above Average) is only better than Brian Elliott, Dan Ellis and Nikolai Khabibulin.
That leaves us with the conclusion, that if goalies performance is highly variable, NHL GM's should spend more of their capped payroll on other positions and hope for the best at the goalie position.
That is a great question, so I thought that I would use the data on the total salaries each team's top two goalies and compare that with each team's performance, first to see if the idea is on target and to then try to explain these counter-intuitive results.
In order to figure this out, I collected each NHL team's standings data (Games Played (GP), Wins (W), Losses (L), OT, Goals For (GF), Goals Against (GA), Standings Points (PTS)) from nhl.com as of today (January 28th, 2011). Then I included the salary data from the Hockey News (January 31, 2011 edition p. 10) and ran a correlation between standings points and goalie salary. The correlation is -0.2777. Hence the writer is on target saying the teams with higher salaried top two goalies are negatively correlated with standings points at the 2010-2011 NHL All-Star break. As I tell my undergraduate students repeated, correlation does not imply causation. We have to work harder to draw conclusions (i.e. statistical inference) than just calculating the correlation between each NHL team's standings points and the combined salary of the top two NHL goalies on each team. To that I now turn.
In order to make a conclusion using statistical techniques, I decided to employ a linear regression on the data that I collected. The regression that I ran was standings points are a function or are determined by Goals For, Goals Against and Games Played. The result was that each variable is statistically significant (i.e. statistically is different from zero at least at a 95% confidence level) and each variable was of the expected sign. The estimated coefficient on Goals For and Games Played were positive (i.e. more goals for and more games played result in more standings points) and the estimated coefficient on Goals Against was negative (i. e. more goals against results in fewer standings points). Yet, when I then included goalie salary, the estimated coefficient was statistically insignificant (i. e. statistically likely to be zero or not relevant). So we can conclude that salary is not a factor in determining standings points up to the All-Star Break this year. Even if we adjust standings points for the number of games played, there still is no statistically significant relationship between team top two goalie salary and team performance.
OK, so there does not seem to be a statistical link between team top two goalie pay and team performance. What about goals against and salary? Shouldn't teams with higher goalie salaries have lower Goals Against? I would think so, but that is not what I find. I find that the estimated coefficient is negative and statistically insignificant - again meaning that there is no statistical relationship between Goals Against and NHL goalie payroll. Nor do I find a statistical relationship between Wins, Losses and NHL goalie payroll.
All of these findings are consistent with the paper Dave Berri and I published in the Journal of Sports Economics last year. We find that goalie salary and goalie performance are not statistically related. Why? Mainly because goalie performance is highly variable from period to period, game to game and season to season.
This is not a dig at NHL goalies. I think that it is really difficult to find an individual who can game after game consistently stop the puck from entering the net at the NHL level, because doing so against the level of NHL talent is so good, not because NHL goalies are not good. What makes people conclude the opposite is that they focus on those goalies (such as Martin Brodeur or Dominic Hasek) who for many years perform at an above average level, they they believe that all goalies can do so. If we look at Brodeur's stats this year, we see that he is (using our NHL goalie measure of Wins Above Average) is only better than Brian Elliott, Dan Ellis and Nikolai Khabibulin.
That leaves us with the conclusion, that if goalies performance is highly variable, NHL GM's should spend more of their capped payroll on other positions and hope for the best at the goalie position.
Thursday, January 27, 2011
NHL Goalie Evaluation at the All-Star Break
OK, my interests are not exclusively about NCAA FBS football. I have done a number of research projects on other sports, and here I turn away from NCAA FBS football, and turn to NHL goalie evaluation.
My good friend and co-author David Berri and I published a paper in the Journal of Sports Economics in 2010 on how GM's evaluate NHL goalies. Basically what we found was that GM's are good at evaluating current player performance by using Vezina voting for the best NHL goalie and using past player performance in setting NHL salaries. Unfortunately, GM's on the whole are not so good at setting salaries in relation to the goalies future performance.
My good friend and co-author David Berri and I published a paper in the Journal of Sports Economics in 2010 on how GM's evaluate NHL goalies. Basically what we found was that GM's are good at evaluating current player performance by using Vezina voting for the best NHL goalie and using past player performance in setting NHL salaries. Unfortunately, GM's on the whole are not so good at setting salaries in relation to the goalies future performance.
I have used this model to evaluate NHL goalies at the end of the 2009-2010 regular season, and have decided to pick up with this evaluation again. So to be clear, here is how we measured NHL goalie performance. WAA is our measure of NHL goalie productivity and is based on the absolute value (since a goal against has a negative effect on team wins) of the marginal value of a goal against divided by two (since each win is worth two standings points) times the number of shots on goal that goalie faces times the difference in the save percentage of the goalie and the average save percentage of all goalies for that season (or in this case up to the trading deadline).
So here are the results for each goalie. The goalie data comes from nhl.com. Hopefully, there is no surprise as to the best goalie in the league (Tim Thomas), who is having a sensational season up the All-Star break. Some of the other results (both positive and negative) were surprising to me.
So here are the results for each goalie. The goalie data comes from nhl.com. Hopefully, there is no surprise as to the best goalie in the league (Tim Thomas), who is having a sensational season up the All-Star break. Some of the other results (both positive and negative) were surprising to me.
| Player | Team | WAA | ||
| Tim Thomas | BOS | 4.754 | ||
| Jonas Hiller | ANA | 2.736 | ||
| Ondrej Pavelec | ATL | 2.358 | ||
| Pekka Rinne | NSH | 2.099 | ||
| Henrik Lundqvist | NYR | 1.985 | ||
| Roberto Luongo | VAN | 1.875 | ||
| Marc-Andre Fleury | PIT | 1.796 | ||
| Tomas Vokoun | FLA | 1.714 | ||
| Carey Price | MTL | 1.591 | ||
| Cam Ward | CAR | 1.570 | ||
| Niklas Backstrom | MIN | 1.365 | ||
| Jonathan Quick | LAK | 1.142 | ||
| Sergei Bobrovsky | PHI | 1.103 | ||
| Semyon Varlamov | WSH | 1.079 | ||
| Kari Lehtonen | DAL | 0.969 | ||
| Ilya Bryzgalov | PHX | 0.968 | ||
| Dwayne Roloson | NYI, TBL | 0.832 | ||
| Corey Crawford | CHI | 0.824 | ||
| Tuukka Rask | BOS | 0.788 | ||
| Brent Johnson | PIT | 0.689 | ||
| James Reimer | TOR | 0.631 | ||
| Cory Schneider | VAN | 0.618 | ||
| Scott Clemmensen | FLA | 0.567 | ||
| Ryan Miller | BUF | 0.485 | ||
| Andrew Raycroft | DAL | 0.457 | ||
| Devan Dubnyk | EDM | 0.424 | ||
| Cedrick Desjardins | TBL | 0.412 | ||
| Brian Boucher | PHI | 0.384 | ||
| Martin Biron | NYR | 0.377 | ||
| Kevin Poulin | NYI | 0.359 | ||
| Anders Lindback | NSH | 0.334 | ||
| Alex Auld | MTL | 0.330 | ||
| Anton Khudobin | MIN | 0.311 | ||
| Martin Gerber | EDM | 0.271 | ||
| Jose Theodore | MIN | 0.243 | ||
| Antti Niemi | SJS | 0.238 | ||
| Jason LaBarbera | PHX | 0.225 | ||
| Michal Neuvirth | WSH | 0.169 | ||
| Matt Climie | PHX | 0.050 | ||
| Richard Bachman | DAL | 0.040 | ||
| Braden Holtby | WSH | -0.023 | ||
| Pascal Leclaire | OTT | -0.056 | ||
| Curtis McElhinney | ANA | -0.065 | ||
| Joey MacDonald | DET | -0.072 | ||
| Jacob Markstrom | FLA | -0.081 | ||
| Mark Dekanich | NSH | -0.081 | ||
| Michael Leighton | PHI | -0.081 | ||
| Robin Lehner | OTT | -0.101 | ||
| Mike McKenna | NJD | -0.102 | ||
| Jhonas Enroth | BUF | -0.173 | ||
| Peter Mannino | ATL | -0.192 | ||
| Chris Osgood | DET | -0.216 | ||
| Jaroslav Halak | STL | -0.218 | ||
| Nathan Lawson | NYI | -0.244 | ||
| Henrik Karlsson | CGY | -0.306 | ||
| Mike Brodeur | OTT | -0.353 | ||
| Patrick Lalime | BUF | -0.355 | ||
| Jimmy Howard | DET | -0.409 | ||
| Mathieu Garon | CBJ | -0.442 | ||
| Antero Niittymaki | SJS | -0.522 | ||
| Craig Anderson | COL | -0.628 | ||
| Johan Hedberg | NJD | -0.661 | ||
| Jonathan Bernier | LAK | -0.680 | ||
| Marty Turco | CHI | -0.856 | ||
| Jean-Sebastien Giguere | TOR | -0.865 | ||
| Miikka Kiprusoff | CGY | -0.985 | ||
| Justin Peters | CAR | -1.052 | ||
| Chris Mason | ATL | -1.086 | ||
| Steve Mason | CBJ | -1.111 | ||
| Peter Budaj | COL | -1.149 | ||
| Ty Conklin | STL | -1.173 | ||
| Mike Smith | TBL | -1.184 | ||
| Jonas Gustavsson | TOR | -1.299 | ||
| Rick DiPietro | NYI | -1.318 | ||
| Martin Brodeur | NJD | -1.372 | ||
| Brian Elliott | OTT | -1.496 | ||
| Dan Ellis | TBL | -1.662 | ||
| Nikolai Khabibulin | EDM | -1.928 |
Wednesday, January 26, 2011
Final NCAA FBS Top 25
OK, I am rather late with the top 25. I was on vacation and then the semester started, and this got dropped to the bottom of the to do list. Anyway, here is the production model's top 25 for the NCAA FBS for the 2010 year.
Later on this year I plan on looking at competitive balance in the NCAA FBS and also post the entire 120 NCAA FBS schools ranking. Who knows, maybe I will also find some time to do some other things as well.
| Rank | School | |
| 1 | Boise State | |
| 2 | TCU | |
| 3 | Oregon | |
| 4 | Alabama | |
| 5 | Ohio State | |
| 6 | Northern Illinois | |
| 7 | Stanford | |
| 8 | Oklahoma State | |
| 9 | Wisconsin | |
| 10 | Nevada | |
| 11 | Hawai'i | |
| 12 | Oklahoma | |
| 13 | Auburn | |
| 14 | UCF | |
| 15 | Iowa | |
| 16 | West Virginia | |
| 17 | Arkansas | |
| 18 | Air Force | |
| 19 | Virginia Tech | |
| 20 | Florida State | |
| 21 | Tulsa | |
| 22 | Missouri | |
| 23 | Pittsburgh | |
| 24 | Nebraska | |
| 25 | Miami (Florida) |
Later on this year I plan on looking at competitive balance in the NCAA FBS and also post the entire 120 NCAA FBS schools ranking. Who knows, maybe I will also find some time to do some other things as well.
Thursday, January 20, 2011
2010-2011 Bowl Results
Well the model went 24-11 this college bowl season. Not bad, given there were some significant upsets (in my opinion - notably North Carolina State defeating West Virginia and Washington defeating Nebraska). Hopefully, tomorrow I will have the final production numbers done and put the production model's final Top 25 on the blog.
Monday, January 10, 2011
BCS Title Game
Finally the BCS title game as chosen by the BCS polls and computer models pitting the #3 most productive team in the nation - Oregon - against the #12 most productive team in the nation - Auburn.
BCS Title Game: Auburn (13-0) vs. Oregon (12-0)
Auburn (#12) overall in production has an offense that is ranked #7 and a defense that is ranked #57. Auburn also has a SOS of 49.85, with their best win over #4 Alabama in terms of overall production.
Oregon (#3) overall in production has an offense that is ranked #1 and a defense that is ranked #8. Oregon also has a SOS of 75.00, with their best win over Stanford (ranked #11 in overall production).
What will be interesting to see is how well Oregon's offense and Auburn's defense play. To me this is the most interesting match up on the field. I think whichever side of this match-up has the best game will determine the outcome, which may not be the conventional wisdom. Thus based only on the production model Oregon is favored over Auburn. So let's stay with the model and choose Oregon.
BCS Title Game: Auburn (13-0) vs. Oregon (12-0)
Auburn (#12) overall in production has an offense that is ranked #7 and a defense that is ranked #57. Auburn also has a SOS of 49.85, with their best win over #4 Alabama in terms of overall production.
Oregon (#3) overall in production has an offense that is ranked #1 and a defense that is ranked #8. Oregon also has a SOS of 75.00, with their best win over Stanford (ranked #11 in overall production).
What will be interesting to see is how well Oregon's offense and Auburn's defense play. To me this is the most interesting match up on the field. I think whichever side of this match-up has the best game will determine the outcome, which may not be the conventional wisdom. Thus based only on the production model Oregon is favored over Auburn. So let's stay with the model and choose Oregon.
Subscribe to:
Posts (Atom)