Loading ...
Sorry, an error occurred while loading the content.

Re: [APBR_analysis] Re: Measuring clutch performance

Expand Messages
  • Ed Weiland
    ... Not only that, baseball teams with a good winning pct. in one-run games generally decline the following season (as myself and several other White Sox fans
    Message 1 of 19 , Dec 1, 2001
    View Source
    • 0 Attachment
      --- John Maxwell <John.Maxwell@...>
      wrote:
      > > "Clutch" play by a team is often reflected in the
      > percent of close
      > > games they win.
      >
      > Actually, at least in baseball and the WNBA, this
      > has not proven to be the
      > case. Generally speaking, bad teams win more close
      > games than any other kind
      > and good teams win fewer close games than any other
      > kind.
      >
      Not only that, baseball teams with a good winning pct.
      in one-run games generally decline the following
      season (as myself and several other White Sox fans
      found out this past summer). I suspect the same is
      true in the NBA, though I have never looked at the
      subject, nor am I aware of anyone who has.

      I would have no idea how to analyze which players are
      clutch and which ones aren't. Basketball isn't like
      baseball where you can just look at what each player
      does in each AB and go from there. In basketball
      there's defense, rebounding and passing going on in
      addition to shooting. Those things would have to be
      looked at also, once clutch situations were defined.

      I've always felt "clutch" was one of those terms
      people used to describe players they wanted to like.
      Jerry West was called Mr. Clutch, despite being on the
      losing team in eight NBA finals and winning only once.
      This isn't to say West wasn't a clutch player. I just
      wonder why West got tagged with Mr. Clutch, when it
      was Bill Russell who was the biggest winner of that
      time. Probably a racial thing.


      Ed


      __________________________________________________
      Do You Yahoo!?
      Buy the perfect holiday gifts at Yahoo! Shopping.
      http://shopping.yahoo.com
    • Michael K. Tamada
      ... I agree 100% with the statements that it is a mistake to look at teams records in close games, and to try to call the ones with good records clutch . A
      Message 2 of 19 , Dec 1, 2001
      View Source
      • 0 Attachment
        On Sat, 1 Dec 2001, Ed Weiland wrote:

        > Not only that, baseball teams with a good winning pct.
        > in one-run games generally decline the following
        > season (as myself and several other White Sox fans
        > found out this past summer). I suspect the same is
        > true in the NBA, though I have never looked at the
        > subject, nor am I aware of anyone who has.

        I agree 100% with the statements that it is a mistake to look at teams'
        records in close games, and to try to call the ones with good records
        "clutch".

        A minor quibble with the argument above however: while it is indeed true
        that baseball teams with a good winning pct. in one-run games can be
        expected to decline the following season, the same is true of ANY team in
        ANY sport in ANY sort of games. Bill James many years ago thought he'd
        discovered some profound truth in this and dreamt up some corny name for
        it -- "The Law of Elastic Reboound" or something -- but it's been known
        for about a century in statistics as "regression to the mean".

        A team which wins 72 games in an NBA season is EXTREMELY likely to have
        fewer wins the following season. A team which wins 90% of its 1-point
        games in a season is extremely likely to win a lower percentage the
        following season. A team which wins 70% of its 1-point games is very
        likely to win a lower pct. the following season. Etc.

        So while I agree 100% with both of the statements (that teams' 1-run
        records have little meaning, except of course to contribute to their
        win-loss record; and that teams with good 1-run records are likely to see
        a decline in those records the following season), it is not the case that
        the latter statement is evidence in favor of the former statement.

        > I would have no idea how to analyze which players are
        > clutch and which ones aren't. Basketball isn't like
        > baseball where you can just look at what each player
        > does in each AB and go from there. In basketball
        > there's defense, rebounding and passing going on in
        > addition to shooting. Those things would have to be
        > looked at also, once clutch situations were defined.

        True enough if we're looking for "total clutchness" but most of the NBA
        players who are known as clutch are known for being clutch as shooters
        during crunch time. Maybe once in a very long while they'll get a
        reputation for good D in crunch time (Havlicek steals the ball, Bird
        steals the ball), and I can't think of a single player who had a
        reputation as a clutch rebounder. Maybe, say, Wilt, Russell, Silas, et
        al -- but they were simply known as great rebounders period, it's not as
        if people thought they only grabbed rebounds during crunch time and
        lollygagged the rest of the game.

        So to look for clutch players, I think it's an easy step to limit the
        search to being a search for clutch *shooters*, and that is a more
        limited, specific, easy-to-define concept.

        > I've always felt "clutch" was one of those terms
        > people used to describe players they wanted to like.
        > Jerry West was called Mr. Clutch, despite being on the
        > losing team in eight NBA finals and winning only once.
        > This isn't to say West wasn't a clutch player. I just
        > wonder why West got tagged with Mr. Clutch, when it
        > was Bill Russell who was the biggest winner of that
        > time. Probably a racial thing.

        I agree with this also, although I would add the following hypothesis:
        some players are given (or demand) the ball a lot in clutch situations.
        And they thus shoot a lot of those crucial shots. I have no idea if some
        players have a systematically higher probability of making those shots,
        but if they take enough of them, some of them will go in. And people will
        remember those, and tend to forget the shots that they missed. And that
        will lead to the player getting a clutch reputation.

        E.g. maybe Jerry West shot in his career 100 clutch shots, and made 47 of
        them. That'd be identical to his career shooting percentage (both regular
        season and playoff). So unless there's a tendency for clutch shots to
        have a lower percentage overall (which actually might be the case), Jerry
        West shot no better in clutch situations than in non-clutch. But
        sportswriters, fans, and coaches would remember those 47 clutch shots
        made, whereas maybe Wilt only made 15 and Gail Goodrich only made 8, and
        thus Jerry West would get the reputation as Mr. Clutch.

        I would add that the notion that Mike Goodman and others have advocated,
        of looking at playoff games as clutch situations, is I think a good one,
        and the fact that West's FG% was as high in the playoffs as it was in the
        regular season is in itself a fairly remarkable, one might even say
        clutch, performance. Especially given that his scoring per game INCRASED.


        --MKT
      • Ed Weiland
        ... Increased shooting could also be a case of a player trying to shoulder too much of the load. It s interesting that in West s case the season his team
        Message 3 of 19 , Dec 2, 2001
        View Source
        • 0 Attachment
          --- "Michael K. Tamada" <tamada@...> wrote:
          >
          > I would add that the notion that Mike Goodman and
          > others have advocated,
          > of looking at playoff games as clutch situations, is
          > I think a good one,
          > and the fact that West's FG% was as high in the
          > playoffs as it was in the
          > regular season is in itself a fairly remarkable, one
          > might even say
          > clutch, performance. Especially given that his
          > scoring per game INCRASED.


          Increased shooting could also be a case of a player
          trying to shoulder too much of the load. It's
          interesting that in West's case the season his team
          finally broke through and won the championship, 1972,
          was the only year he averaged fewer points in the
          playoffs than the regular season. West also shot only
          .376 during the 1972 playoffs, by far the worst
          showing of his career. He did post a career playoff
          high in assists per game during the '72 playoffs.

          Here are some other championship performances:

          Wilt in '67 averaged a then career-low 21.7 ppg in the
          playoffs, shot 104 points below his regular season FG
          pct. (albeit a more-than-adequate .579), but posted a
          career high with 9.0 assists per game.

          Hakeem in '94 and '95 had FG pct. similar to his
          regular season and career totals, but posted two of
          his three highest playoff assist per game totals, 4.5
          and 4.3 apg, both well above his career playoff
          average of 3.3. Hakeem scored 33.0 ppg in the '95
          playoffs, so it's not like he was sacrificing his
          shots.

          I'm not sure if the spike in assists is most
          responsible for the championships, but I don't think
          it can be ignored. Especially considering that star
          players who aren't point guards, but possessed
          good-to-great passing skills like Russell, Barry,
          Walton, Bird and Jordan tended to win championships.
          Sometimes the the most clutch thing for a player to do
          is to get his teammates involved.

          btw, I don't mean to knock West as non-clutch. HIs
          Laker teams lost three game sevens to the Celtics by a
          total of seven points. There had to be some bad luck
          involved in all that.

          Ed Weiland

          __________________________________________________
          Do You Yahoo!?
          Buy the perfect holiday gifts at Yahoo! Shopping.
          http://shopping.yahoo.com
        • harlanzo@yahoo.com
          When considering clutch it seems weird to think about players actually improving over how they would in normal (nonpressure) situations. Rather, it seems to
          Message 4 of 19 , Dec 2, 2001
          View Source
          • 0 Attachment
            When considering clutch it seems weird to think about players
            actually improving over how they would in normal (nonpressure)
            situations. Rather, it seems to me that we might better define
            clutch by looking at who did not become worse in clutch situations.
            How you define clutch situations, incidentally, is a question I can't
            really answer.

            --- In APBR_analysis@y..., Ed Weiland <weiland1029@y...> wrote:
            >
            > --- "Michael K. Tamada" <tamada@o...> wrote:
            > >
            > > I would add that the notion that Mike Goodman and
            > > others have advocated,
            > > of looking at playoff games as clutch situations, is
            > > I think a good one,
            > > and the fact that West's FG% was as high in the
            > > playoffs as it was in the
            > > regular season is in itself a fairly remarkable, one
            > > might even say
            > > clutch, performance. Especially given that his
            > > scoring per game INCRASED.
            >
            >
            > Increased shooting could also be a case of a player
            > trying to shoulder too much of the load. It's
            > interesting that in West's case the season his team
            > finally broke through and won the championship, 1972,
            > was the only year he averaged fewer points in the
            > playoffs than the regular season. West also shot only
            > .376 during the 1972 playoffs, by far the worst
            > showing of his career. He did post a career playoff
            > high in assists per game during the '72 playoffs.
            >
            > Here are some other championship performances:
            >
            > Wilt in '67 averaged a then career-low 21.7 ppg in the
            > playoffs, shot 104 points below his regular season FG
            > pct. (albeit a more-than-adequate .579), but posted a
            > career high with 9.0 assists per game.
            >
            > Hakeem in '94 and '95 had FG pct. similar to his
            > regular season and career totals, but posted two of
            > his three highest playoff assist per game totals, 4.5
            > and 4.3 apg, both well above his career playoff
            > average of 3.3. Hakeem scored 33.0 ppg in the '95
            > playoffs, so it's not like he was sacrificing his
            > shots.
            >
            > I'm not sure if the spike in assists is most
            > responsible for the championships, but I don't think
            > it can be ignored. Especially considering that star
            > players who aren't point guards, but possessed
            > good-to-great passing skills like Russell, Barry,
            > Walton, Bird and Jordan tended to win championships.
            > Sometimes the the most clutch thing for a player to do
            > is to get his teammates involved.
            >
            > btw, I don't mean to knock West as non-clutch. HIs
            > Laker teams lost three game sevens to the Celtics by a
            > total of seven points. There had to be some bad luck
            > involved in all that.
            >
            > Ed Weiland
            >
            > __________________________________________________
            > Do You Yahoo!?
            > Buy the perfect holiday gifts at Yahoo! Shopping.
            > http://shopping.yahoo.com
          • igor eduardo küpfer
            ... Here s the correlation matrix. (I hope it formats ok.) Days Dist Home MatchupP Dist 0.076 0.000 Home 0.173 -0.249 0.000 0.000
            Message 5 of 19 , May 30, 2004
            View Source
            • 0 Attachment
              Dean Oliver wrote:
              > 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.

              Here's the correlation matrix. (I hope it formats ok.)

              Days Dist Home MatchupP
              Dist 0.076
              0.000

              Home 0.173 -0.249
              0.000 0.000

              MatchupP -0.014 0.021 0.001
              0.509 0.315 0.975

              PtsDiff 0.060 -0.041 0.226 0.465
              0.003 0.048 0.000 0.000

              Cell Contents: Pearson correlation
              P-Value

              The correlations are generally pretty low.

              > (Something also irks me about the p_win variable being
              > endogenous.)
              >

              I'm not quite sure what endogenous means. If it means being related to the
              other variables, I'm not quite sure if that's true: the matchup probability
              calculation uses only team winning percentage and opponent winning
              percentage, neither of which have any relationship to the other variables.
              Maybe I misunderstood.


              > 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.
              >

              I did something like that, too. I can't remember which season I used, but I
              found that most wins came on 2 day rests (I think). However, I didn't
              include any other variables, so I could have just been looking at a
              scheduling quirk for that season. I'll probably rerun this study on another
              season to see if the results hold. If anyone else wants to give it a shot,
              here's a table showing travel distances between NBA cities:

              http://members.rogers.com/brothered/junk/TravelDistances.htm

              ed
            • Dean Oliver
              ... Yeah, pretty low. Probably not much to worry about. ... to the ... probability ... variables. ... Basically, I assume you use 2004 win-loss records to
              Message 6 of 19 , May 30, 2004
              View Source
              • 0 Attachment
                --- In APBR_analysis@yahoogroups.com, igor eduardo küpfer
                <edkupfer@r...> wrote:
                > Dean Oliver wrote:
                > > 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.
                >
                > Here's the correlation matrix. (I hope it formats ok.)
                >
                > Days Dist Home MatchupP
                > Dist 0.076
                > 0.000
                >
                > Home 0.173 -0.249
                > 0.000 0.000
                >
                > MatchupP -0.014 0.021 0.001
                > 0.509 0.315 0.975
                >
                > PtsDiff 0.060 -0.041 0.226 0.465
                > 0.003 0.048 0.000 0.000
                >
                > Cell Contents: Pearson correlation
                > P-Value
                >
                > The correlations are generally pretty low.
                >

                Yeah, pretty low. Probably not much to worry about.

                > > (Something also irks me about the p_win variable being
                > > endogenous.)
                > >
                >
                > I'm not quite sure what endogenous means. If it means being related
                to the
                > other variables, I'm not quite sure if that's true: the matchup
                probability
                > calculation uses only team winning percentage and opponent winning
                > percentage, neither of which have any relationship to the other
                variables.
                > Maybe I misunderstood.

                Basically, I assume you use 2004 win-loss records to evaluate p_win.
                Well, those win-loss records are built from the things you are looking
                at -- whether a team is at home or on the road, how many days off,
                their whole schedule. Maybe the win-loss records of teams prior to
                the matchup of the game you're looking at is exogenous (known a
                priori). i.e., San Antonio faces the Lakers when one team is 12-5 and
                the other is 10-7 -- use those records rather than their end of season
                records. Maybe that's what you're doing, I dunno. I have doubt that
                it would make a significant difference.


                >
                >
                > > 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.
                > >
                >
                > I did something like that, too. I can't remember which season I
                used, but I
                > found that most wins came on 2 day rests (I think). However, I didn't
                > include any other variables, so I could have just been looking at a
                > scheduling quirk for that season. I'll probably rerun this study on
                another
                > season to see if the results hold.

                Just include the variable Days^2 in your regression and rerun that.
                See what comes out significant.

                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
              • igor eduardo küpfer
                Dean Oliver wrote: ... Ah. I will try to use contemporary win/loss records in my next analysis. ... You ll have to help me out here, as I don t
                Message 7 of 19 , May 30, 2004
                View Source
                • 0 Attachment
                  Dean Oliver wrote:
                  <snip>

                  >>>
                  >>
                  >> I'm not quite sure what endogenous means. If it means being related
                  > to the
                  >> other variables, I'm not quite sure if that's true: the matchup
                  >> probability calculation uses only team winning percentage and
                  >> opponent winning percentage, neither of which have any relationship
                  >> to the other variables. Maybe I misunderstood.
                  >
                  > Basically, I assume you use 2004 win-loss records to evaluate p_win.
                  > Well, those win-loss records are built from the things you are looking
                  > at -- whether a team is at home or on the road, how many days off,
                  > their whole schedule. Maybe the win-loss records of teams prior to
                  > the matchup of the game you're looking at is exogenous (known a
                  > priori). i.e., San Antonio faces the Lakers when one team is 12-5 and
                  > the other is 10-7 -- use those records rather than their end of season
                  > records. Maybe that's what you're doing, I dunno. I have doubt that
                  > it would make a significant difference.

                  Ah. I will try to use contemporary win/loss records in my next analysis.

                  <snip>

                  > Just include the variable Days^2 in your regression and rerun that.
                  > See what comes out significant.
                  >

                  You'll have to help me out here, as I don't know anything about transforming
                  data. Do you mean include Days^2 in addition to Days or instead of Days? I
                  did both, and here's how they turned out:

                  PtsDiff = - 21.2 + 2.59 Days +0.000021 Dist + 5.59 Home + 30.0 MatchupP -
                  0.394 Days_2

                  Predictor Coef SE Coef T P
                  Constant -21.169 1.195 -17.71 0.000
                  Days 2.5924 0.7956 3.26 0.001
                  Dist 0.0000214 0.0003555 0.06 0.952
                  Home 5.5937 0.4858 11.51 0.000
                  MatchupP 29.991 1.134 26.45 0.000
                  Days_2 -0.3938 0.1350 -2.92 0.004

                  PtsDiff = - 18.1 +0.000099 Dist + 5.87 Home + 29.9 MatchupP + 0.0236 Days_2

                  Predictor Coef SE Coef T P
                  Constant -18.0811 0.7304 -24.76 0.000
                  Dist 0.0000990 0.0003555 0.28 0.781
                  Home 5.8677 0.4794 12.24 0.000
                  MatchupP 29.890 1.136 26.32 0.000
                  Days_2 0.02362 0.04275 0.55 0.581


                  ed
                • Dean Oliver
                  ... I should note that, not being an economist, I like throwing this word around without as great an appreciation or understanding for it as I should. ...
                  Message 8 of 19 , May 30, 2004
                  View Source
                  • 0 Attachment
                    --- In APBR_analysis@yahoogroups.com, igor eduardo küpfer
                    <edkupfer@r...> wrote:
                    > >> I'm not quite sure what endogenous means. If it means being

                    I should note that, not being an economist, I like throwing this word
                    around without as great an appreciation or understanding for it as I
                    should.


                    > You'll have to help me out here, as I don't know anything about
                    transforming
                    > data. Do you mean include Days^2 in addition to Days or instead of
                    Days? I
                    > did both, and here's how they turned out:

                    Include both, which you did in the first set below. Looks like it got
                    you significant on both days and days^2. And the signs are as
                    expected. It suggests optimal rest at about 3 days, longer than the 2
                    days we saw before. (Potentially important for the talk about rust vs
                    rest, esp if the Lakers wrap up on M.) Let me also ask -- is Days = 0
                    if a team plays back to back nights or is that Days = 1?

                    I'm sure there are other ways to manipulate things, but this looks
                    like a pretty good thing. I'm saving it.

                    Home is a binary 1/0 indicator for home/road, resp?

                    >
                    > PtsDiff = - 21.2 + 2.59 Days +0.000021 Dist + 5.59 Home + 30.0
                    MatchupP -
                    > 0.394 Days_2
                    >
                    > Predictor Coef SE Coef T P
                    > Constant -21.169 1.195 -17.71 0.000
                    > Days 2.5924 0.7956 3.26 0.001
                    > Dist 0.0000214 0.0003555 0.06 0.952
                    > Home 5.5937 0.4858 11.51 0.000
                    > MatchupP 29.991 1.134 26.45 0.000
                    > Days_2 -0.3938 0.1350 -2.92 0.004
                    >
                    >

                    DeanO

                    Dean Oliver
                    Author, Basketball on Paper
                    http://www.basketballonpaper.com
                    "Dean Oliver looks at basketball with a fresh perspective. If you
                    want a new way to analyze the game, this book is for you. You'll
                    never watch a game the same way again. We use his stuff and it helps
                    us." Yvan Kelly, Scout, Seattle Sonics
                  • igor eduardo küpfer
                    Okay, I ran the test again, this time using 03-04 results. Before I show you what I got, let me address a couple of things. ... Hell, that s nothing. Once
                    Message 9 of 19 , May 31, 2004
                    View Source
                    • 0 Attachment
                      Okay, I ran the test again, this time using 03-04 results. Before I show you
                      what I got, let me address a couple of things.

                      Dean Oliver wrote:
                      > --- In APBR_analysis@yahoogroups.com, igor eduardo küpfer
                      > <edkupfer@r...> wrote:
                      >>>> I'm not quite sure what endogenous means. If it means being
                      >
                      > I should note that, not being an economist, I like throwing this word
                      > around without as great an appreciation or understanding for it as I
                      > should.

                      Hell, that's nothing. Once during the course of an argument with an
                      ex-girlfriend I used the word "heretofore." I still don't know what it
                      means.

                      >
                      >> You'll have to help me out here, as I don't know anything about
                      >> transforming data. Do you mean include Days^2 in addition to Days or
                      >> instead of Days? I did both, and here's how they turned out:
                      >
                      > Include both, which you did in the first set below. Looks like it got
                      > you significant on both days and days^2. And the signs are as
                      > expected. It suggests optimal rest at about 3 days, longer than the 2
                      > days we saw before. (Potentially important for the talk about rust vs
                      > rest, esp if the Lakers wrap up on M.)

                      Questions: I don't understand a couple of things about the squared term. How
                      did you know that squaring the Days variable would give a better fit? And,
                      just exactly how does it suggest the optimal 3 day rest?

                      > Let me also ask -- is Days = 0
                      > if a team plays back to back nights or is that Days = 1?
                      >

                      The latter. I am subtracting game dates from each other.

                      > I'm sure there are other ways to manipulate things, but this looks
                      > like a pretty good thing. I'm saving it.
                      >
                      > Home is a binary 1/0 indicator for home/road, resp?

                      Yes.

                      Okay. Here are the results for 03-04. For the Matchup Probability, I used
                      the team records heading into the game. For example, for two teams playing
                      their first games of the season, I would use 0-0 records for each team in my
                      probability calculation. Interestingly, this doesn't seem to affect the
                      regression results too much. The effect of Days between games is reduced in
                      this sample. Weird.


                      PtsDiff = - 13.6 + 7.31 Home +0.000027 Distance + 18.1 WinProb + 0.722
                      Days - 0.122 Days^2

                      Predictor Coef SE Coef T P
                      Constant -13.582 1.173 -11.58 0.000
                      Home 7.3056 0.5010 14.58 0.000
                      Distance 0.0000269 0.0003734 0.07 0.943
                      WinProb 18.054 1.163 15.53 0.000
                      Days 0.7221 0.7202 1.00 0.316
                      Days^2 -0.1216 0.1138 -1.07 0.286

                      S = 11.48 R-Sq = 16.7% R-Sq(adj) = 16.6%

                      Analysis of Variance

                      Source DF SS MS F P
                      Regression 5 62072 12414 94.19 0.000
                      Residual Error 2343 308806 132
                      Total 2348 370877

                      ed
                    • Dean Oliver
                      ... show you ... I ve had those moments, often inspired by arguments with soon-to-be ex-girlfriends. What the hell is vis-a-vis ? ... term. How ... fit? And,
                      Message 10 of 19 , May 31, 2004
                      View Source
                      • 0 Attachment
                        --- In APBR_analysis@yahoogroups.com, igor eduardo küpfer
                        <edkupfer@r...> wrote:
                        > Okay, I ran the test again, this time using 03-04 results. Before I
                        show you
                        > what I got, let me address a couple of things.
                        >
                        > Dean Oliver wrote:
                        > > --- In APBR_analysis@yahoogroups.com, igor eduardo küpfer
                        > > <edkupfer@r...> wrote:
                        > >>>> I'm not quite sure what endogenous means. If it means being
                        > >
                        > > I should note that, not being an economist, I like throwing this word
                        > > around without as great an appreciation or understanding for it as I
                        > > should.
                        >
                        > Hell, that's nothing. Once during the course of an argument with an
                        > ex-girlfriend I used the word "heretofore." I still don't know what it
                        > means.

                        I've had those moments, often inspired by arguments with soon-to-be
                        ex-girlfriends. What the hell is "vis-a-vis"?

                        > >
                        > >> You'll have to help me out here, as I don't know anything about
                        > >> transforming data. Do you mean include Days^2 in addition to Days or
                        > >> instead of Days? I did both, and here's how they turned out:
                        > >
                        > > Include both, which you did in the first set below. Looks like it got
                        > > you significant on both days and days^2. And the signs are as
                        > > expected. It suggests optimal rest at about 3 days, longer than the 2
                        > > days we saw before. (Potentially important for the talk about rust vs
                        > > rest, esp if the Lakers wrap up on M.)
                        >
                        > Questions: I don't understand a couple of things about the squared
                        term. How
                        > did you know that squaring the Days variable would give a better
                        fit? And,
                        > just exactly how does it suggest the optimal 3 day rest?

                        I didn't _know_ it would give a better fit. I hoped it would because
                        of what we were observing -- that there was an optimal number of days
                        off. The only way to get an optimum out of a regression is to throw
                        in higher order terms. Usually a squared term is plenty. It doesn't
                        answer the bigger question of whether teams get rusty, though. It
                        suggests an answer (another lesson in how to lie with statistics), one
                        that I wouldn't trust from this study.

                        Look at the results of your regression. Take just the Days and Days^2
                        coefficients and calculate the marginal net points those terms
                        contribute for Days = 1, 2, 3, 4, etc. You'll see a max at 3.

                        >
                        > > Let me also ask -- is Days = 0
                        > > if a team plays back to back nights or is that Days = 1?
                        > >
                        >
                        > The latter. I am subtracting game dates from each other.
                        >

                        So 2 days of rest is optimal.

                        > > I'm sure there are other ways to manipulate things, but this looks
                        > > like a pretty good thing. I'm saving it.
                        > >
                        > > Home is a binary 1/0 indicator for home/road, resp?
                        >
                        > Yes.
                        >
                        > Okay. Here are the results for 03-04. For the Matchup Probability, I
                        used
                        > the team records heading into the game. For example, for two teams
                        playing
                        > their first games of the season, I would use 0-0 records for each
                        team in my
                        > probability calculation.

                        I was curious to see how you handled the early games of the season,
                        especially the times where one team was undefeated. It looks like you
                        used Pythagorean projections, rather than real records anyway. That
                        helps. But 0-0 usually requires some other assumption, like a
                        Bayesian prior that carries through the first few games.

                        >Interestingly, this doesn't seem to affect the
                        > regression results too much. The effect of Days between games is
                        reduced in
                        > this sample. Weird.

                        Not sure what to make of that weakening of the Days. What was the R2
                        of the previous version? We may have to improve the prior matchup P
                        to get back a reasonable estimate of the value of Days. If you just
                        look at games beyond the first 20 in the season, does r2 get better
                        and does Days become more significant?

                        >
                        >
                        > PtsDiff = - 13.6 + 7.31 Home +0.000027 Distance + 18.1 WinProb + 0.722
                        > Days - 0.122 Days^2
                        >
                        > Predictor Coef SE Coef T P
                        > Constant -13.582 1.173 -11.58 0.000
                        > Home 7.3056 0.5010 14.58 0.000
                        > Distance 0.0000269 0.0003734 0.07 0.943
                        > WinProb 18.054 1.163 15.53 0.000
                        > Days 0.7221 0.7202 1.00 0.316
                        > Days^2 -0.1216 0.1138 -1.07 0.286
                        >
                        > S = 11.48 R-Sq = 16.7% R-Sq(adj) = 16.6%
                        >
                        > Analysis of Variance
                        >
                        > Source DF SS MS F P
                        > Regression 5 62072 12414 94.19 0.000
                        > Residual Error 2343 308806 132
                        > Total 2348 370877
                        >

                        DeanO

                        Dean Oliver
                        Author, Basketball on Paper
                        http://www.basketballonpaper.com
                        "Excellent writing. There are a lot of math guys who just rush from
                        the numbers to the conclusion. . .they'll tell you that Shaq is a real
                        good player but his team would win a couple more games a year if he
                        could hit a free throw. Dean is more than that; he's really
                        struggling to understand the actual problem, rather than the
                        statistical after-image of it. I learn a lot by reading him." Bill
                        James, author Baseball Abstract
                      • igor eduardo küpfer
                        Replies to DanR and DeanO ... I m sorry I didn t make it clear. For the second analysis (on the 03-04 regular season results) I didn;t use Pythagorean records.
                        Message 11 of 19 , Jun 2, 2004
                        View Source
                        • 0 Attachment
                          Replies to DanR and DeanO

                          Dean Oliver wrote:

                          > I was curious to see how you handled the early games of the season,
                          > especially the times where one team was undefeated. It looks like you
                          > used Pythagorean projections, rather than real records anyway. That
                          > helps. But 0-0 usually requires some other assumption, like a
                          > Bayesian prior that carries through the first few games.

                          I'm sorry I didn't make it clear. For the second analysis (on the 03-04
                          regular season results) I didn;t use Pythagorean records. I instead used
                          each team's record to date. Two teams facing each other on the first game of
                          the season each had a 0.5 chance of winning that game, since they had
                          identical 0-0 records.

                          The results don't deviate much from my first analysis, which used season's
                          end Pythagorean Win%. I supposed this is because after the first part of the
                          season, each team's Pyth is relatively stable. I must admit to being a
                          little surprised by this, though.

                          > Not sure what to make of that weakening of the Days. What was the R2
                          > of the previous version?

                          r = 0.06 for 00-01, r = 0.03 for this season.

                          > We may have to improve the prior matchup P
                          > to get back a reasonable estimate of the value of Days. If you just
                          > look at games beyond the first 20 in the season, does r2 get better
                          > and does Days become more significant?
                          >

                          Games 2-20: r = 0.048 (p = 0.261)
                          Games 21-82: r = 0.024 (p = 0.314

                          dan_t_rosenbaum wrote:

                          > Interesting results. Here are a couple of suggestions.
                          >
                          > I would leave out the MatchupP variable, since it is a lot like the
                          > dependent variable. Including it probably increases R-squared a
                          > lot, but probably doesn't do much else. (All in all, it probably is
                          > pretty harmless, since it unlikely to be correlated with your
                          > independent variables.)
                          >
                          > Another option with your day variable is to enter it as a series of
                          > dummy variables.
                          >
                          > DAY0 - equals 1 if 0 days of rest, 0 otherwise
                          > DAY1 - equals 1 if 1 day of rest , 0 otherwise
                          > DAY2 - equals 1 if 2 days of rest, 0 otherwise
                          > DAY3 - equals 1 if 3 days of rest, 0 otherwise
                          > DAY4+ - equals 1 if 4 days or more of rest, 0 otherwise
                          >
                          > Then run the regression leaving one of those variables out.
                          >
                          > If, for example, you left DAY0 out of the regression, the DAY1
                          > coefficient would give you the effect of playing on one day's rest
                          > versus playing in a back-to-back.
                          >
                          > The DAY2 coefficent would give you the effect of playing on two
                          > days' rest versus playing in a back-to-back.
                          >
                          > The DAY3 coefficent would give you the effect of playing on three
                          > days' rest versus playing in a back-to-back.
                          >
                          > The DAY4+ coefficent would give you the effect of playing on four or
                          > more days' rest versus playing in a back-to-back.
                          >

                          Okay, I tried this. The regression outputs follow. I'm afraid that I don't
                          know how to interpret the results -- very few of the coefficients are
                          significant. (Note that I use Day1 to mean 1 day between games, ie back to
                          back -- the 1 does not mean "rest days.")

                          Ommitting Days1

                          Predictor Coef SE Coef T P
                          Constant -3.8515 0.5971 -6.45 0.000
                          Home 7.1257 0.5267 13.53 0.000
                          Distance -0.0000105 0.0003920 -0.03 0.979
                          Days2 0.5837 0.6157 0.95 0.343
                          Days3 0.3022 0.7996 0.38 0.706
                          Days4 -2.363 1.394 -1.70 0.090
                          Days5+ 0.935 1.801 0.52 0.604


                          Omitting Days2

                          Predictor Coef SE Coef T P
                          Constant -3.2678 0.5624 -5.81 0.000
                          Home 7.1257 0.5267 13.53 0.000
                          Distance -0.0000105 0.0003920 -0.03 0.979
                          Days1 -0.5837 0.6157 -0.95 0.343
                          Days3 -0.2815 0.6981 -0.40 0.687
                          Days4 -2.947 1.338 -2.20 0.028
                          Days5+ 0.351 1.758 0.20 0.842


                          Omitting Days3

                          Predictor Coef SE Coef T P
                          Constant -3.5494 0.7833 -4.53 0.000
                          Home 7.1257 0.5267 13.53 0.000
                          Distance -0.0000105 0.0003920 -0.03 0.979
                          Days1 -0.3022 0.7996 -0.38 0.706
                          Days2 0.2815 0.6981 0.40 0.687
                          Days4 -2.665 1.426 -1.87 0.062
                          Days5+ 0.633 1.824 0.35 0.729


                          Omitting Days4

                          Predictor Coef SE Coef T P
                          Constant -6.215 1.385 -4.49 0.000
                          Home 7.1257 0.5267 13.53 0.000
                          Distance -0.0000105 0.0003920 -0.03 0.979
                          Days1 2.363 1.394 1.70 0.090
                          Days2 2.947 1.338 2.20 0.028
                          Days3 2.665 1.426 1.87 0.062
                          Days5+ 3.298 2.152 1.53 0.126

                          Omitting Days5+

                          Predictor Coef SE Coef T P
                          Constant -2.917 1.799 -1.62 0.105
                          Home 7.1257 0.5267 13.53 0.000
                          Distance -0.0000105 0.0003920 -0.03 0.979
                          Days1 -0.935 1.801 -0.52 0.604
                          Days2 -0.351 1.758 -0.20 0.842
                          Days3 -0.633 1.824 -0.35 0.729
                          Days4 -3.298 2.152 -1.53 0.126


                          ed
                        • igor eduardo küpfer
                          ... http://www.shrpsports.com/nba/stand/2002.htm -- ed
                          Message 12 of 19 , Nov 2, 2004
                          View Source
                          • 0 Attachment
                            ivan ivan wrote:
                            > this is a simple question
                            > but i can't find it anywhere....
                            >
                            >
                            > I'm doing analysis on how a history of winning or losing affects your
                            > chances of winning at the end of close games... so does anyone know
                            > where i can standings for the 2001-2002 NBA season?
                            > i want the home and away records?
                            >

                            http://www.shrpsports.com/nba/stand/2002.htm

                            --
                            ed
                          Your message has been successfully submitted and would be delivered to recipients shortly.