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  • Dean Oliver
    May 30 11:10 AM
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      Ed --

      Nice. Is there correlation between variables? One that is key to
      understand is whether distance from previous game and days off are
      correlated. A home stand could be hiding some aspect of time off
      between games. (Something also irks me about the p_win variable being
      endogenous.)

      I know I did a study of time off between games and saw that there is
      an optimal period of time off (more than 2 wasn't good, but neither
      was 0). That would imply a squared term in days off. But I didn't do
      it as rigorously as you did.

      DeanO

      Dean Oliver
      Author, Basketball on Paper
      http://www.basketballonpaper.com
      "Oliver goes beyond stats to dissect what it takes to win. His breezy
      style makes for enjoyable reading, but there are plenty of points of
      wisdom as well. This book can be appreciated by fans, players,
      coaches and executives, but more importantly it can be used as a text
      book for all these groups. You are sure to learn something you didn't
      know about basketball here." Pete Palmer, co-author, Hidden Game of
      Baseball and Hidden Game of Football


      --- In APBR_analysis@yahoogroups.com, igor eduardo küpfer
      <edkupfer@r...> wrote:
      > Whoa! Lotta traffic at apbr_*. To add to the bustle, I post the
      following,
      > an unfinished study of mine on the effects of team travel on winning.
      >
      > ***
      >
      > What are the factors in travel that affect team performance? To begin to
      > answer this question, I regressed the results of the 2000-01 season
      against
      > four variables:
      >
      > Days - Number of days between games, eg back to back games = 1
      > Dist - Distance in miles from location of previous game to next
      > game.[1]
      > Home - Home court dummy, home court = 1 away game = 0
      > MatchupP - Probability of team winning game [2]
      >
      > I used the points differential for each game as the response.
      >
      > I made the following assumption: that teams never return home
      between road
      > games; that is, if the Spurs played in Seattle just before the All-Star
      > break, and played in Miami just following the break, I used the travel
      > distance between Seattle and Miami, even though the Spurs likely
      went back
      > to San Antonio between games. Also, to remove the ambiguity of the
      amount of
      > rest at the beginning of the season, I removed each team's first
      game of the
      > season from my sample.
      >
      > The results show that only the Matchup Probability and Home/Away
      variables
      > are significant at 5%. (Regression results appended to end of this
      post.)
      > The Days Between Games variable is not significant (p = 0.121) but I
      think
      > that may be an artefact of my sample, because I've seen other study
      which
      > show a significant relationship.
      >
      > ***
      >
      > [1 Distance approximated using the following method:
      >
      > Distance between cities = sqrt(x * x + y * y)
      >
      > where x = 69.1 * (lat2 - lat1)
      > and y = 69.1 * (lon2 - lon1) * cos(lat1/57.3) ]
      >
      > [2 Probability of team win calculated using Bill James's log5 method:
      >
      > Pr(team win) = (A - A * B) / (A + B - 2 * A * B)
      >
      > where A and B = team A's and team B's winning percentage,
      respectively. For
      > this study, I used Pythagorean winning percentages instead.]
      >
      >
      > REGRESSION OUTPUT
      >
      > The regression equation is
      > PtsDiff = - 18.7 + 0.391 Days +0.000051 Dist + 5.76 Home + 29.9 MatchupP
      >
      > Predictor Coef SE Coef T P
      > Constant -18.6980 0.8449 -22.13 0.000
      > Days 0.3908 0.2518 1.55 0.121
      > Dist 0.0000513 0.0003559 0.14 0.886
      > Home 5.7589 0.4832 11.92 0.000
      > MatchupP 29.914 1.135 26.35 0.000
      >
      > S = 11.11 R-Sq = 26.9% R-Sq(adj) = 26.7%
      >
      > Analysis of Variance
      >
      > Source DF SS MS F P
      > Regression 4 106275 26569 215.13 0.000
      > Residual Error 2344 289491 124
      > Lack of Fit 2239 278271 124 1.16 0.158
      > Pure Error 105 11220 107
      > Total 2348 395767
      >
      > 2139 rows with no replicates
      >
      > --
      >
      > ed
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