November 29, 2009

Who’s Hot, Who’s Not in the NBA

Filed under: Uncategorized — wwinston @ 12:13 pm

Below we list NBA Adjusted +/- Ratings for players based on their last 5 games. Top 20 and bottom 18 players are listed for players who have averaged 25 minutes or more during their last 5 games. For example, our best estimate (after adjusting for who he played with and against) is that in his last 5 games Marc Gasol has played 33 points better per 48 minutes than an average player.  You can see how the trio of Nash, Richardson, and Frye have sparked the Suns while Stephen Jackson has sparked Charlotte’s recent surge.  

      Note that “stars” like Battier, David Lee, Brandon Roy, J.R. Smith and Chauncey Billups have been struggling lately.

 

                                      POINTS   MINUTES     SIGMA

 

    1  MEM Marc Gasol                   32.76    177.15   ( 13.57)     1

    2  CLE LeBron James                 29.45    189.59   (  9.15)     2

    3  DAL Dirk Nowitzki                28.51    187.55   ( 16.69)     3

    4  NOH Marcus Thornton              25.90    125.87   ( 17.81)     4

    5  OKC Kevin Durant                 25.80    172.95   ( 16.58)     5

    6  LAL Kobe Bryant                  24.49    182.28   ( 11.81)     6

    7  CHI Luol Deng                    24.09    189.63   (  9.54)     7

    8  CHA Raja Bell                    21.57    157.45   ( 16.20)     8

    9  WAS Brendan Haywood              20.63    149.80   ( 15.62)     9

   10  GSW Stephen Curry                19.65    185.98   ( 19.03)    10

 

                                       POINTS   MINUTES     SIGMA

 

   11  ATL Mike Bibby                   18.88    142.14   ( 12.64)    11

   12  GSW Stephen Jackson              18.64    169.31   ( 21.76)    12

   13  PHO Channing Frye                18.41    135.35   ( 14.48)    13

   14  PHO Steve Nash                   18.36    156.72   ( 17.86)    14

   15  MIA Dwyane Wade                  18.10    192.70   ( 11.67)    15

   16  PHO Jason Richardson             17.22    145.16   ( 15.56)    16

   17  DEN Nene                         16.92    165.14   ( 15.19)    17

   18  DAL Shawn Marion                 16.42    141.57   ( 24.13)    18

   19  CHI John Salmons                 15.61    167.90   ( 15.67)    19

   20  CHA Stephen Jackson              13.80    189.97   ( 10.20)    20

 

                                     POINTS   MINUTES     SIGMA
 
  151  HOU Shane Battier               -12.29    155.40   ( 19.92)   151
  152  NOH Peja Stojakovic             -12.44    160.38   ( 16.58)   152
  153  MIN Jonny Flynn                 -13.03    153.44   ( 10.80)   153
  154  ORL Mickael Pietrus             -13.66    138.63   ( 15.30)   154
  155  DEN Chauncey Billups            -14.57    152.80   ( 13.34)   155
  156  GSW Anthony Randolph            -14.77    126.62   ( 18.73)   156
  157  MIL Michael Redd                -14.92    125.51   ( 22.46)   157
  158  DEN J.R. Smith                  -14.97    133.35   ( 12.84)   158
  159  NYK David Lee                   -16.10    164.28   ( 20.35)   159
  160  DET Will Bynum                  -16.11    145.00   ( 12.85)   160
 
                                       POINTS   MINUTES     SIGMA
 
  161  POR Brandon Roy                 -16.50    181.63   ( 11.43)   161
  162  SAC Andres Nocioni              -17.08    139.90   ( 21.03)   162
  163  IND Troy Murphy                 -18.14    141.68   ( 14.02)   163
  164  ATL Marvin Williams             -19.11    167.79   ( 13.39)   164
  165  MIN Al Jefferson                -19.60    178.24   ( 13.37)   165
  166  MIA Jermaine O'Neal             -19.71    168.40   ( 15.23)   166
  167  WAS Gilbert Arenas              -22.60    168.79   ( 15.21)   167
  168  DAL Drew Gooden                 -26.52    125.91   ( 18.60)   168
 

 

November 28, 2009

The Knicks Could be Better!

Filed under: Uncategorized — wwinston @ 3:57 pm

So far this year the Knicks have played around 7 points worse than average.  If they continue to play at this poor level, they will be lucky to win 25 games. Things do not have to be this bad! Even below average teams like the Knicks have many decent lineups. So far for the Knicks here are some of their better lineups. For example, Duhon, Harrington, Gallo, Hughes and Chandler in for 13 minutes has played 68 points better (per 48 minutes) than an average NBA lineup. Together the lineups listed below play around 8 points better than average. The rest of the time the Knicks  lineups play 16 points worse than average. Clearly, for most of the lineups below we have a small sample size. Still, playing these lineups more and other poor lineups less minutes would improve the Knicks level of performance. These lineups use all the Knicks rotation players and it would be easy to devise a sequence of lineups that gave each player their needed minutes and still included only “decent” lineups.

       NYK      -2.88     149.97 minutes  

Chandler      Duhon         Gallinari     Hughes        Lee          

 

NYK      -0.69    33.55 minutes   19

Chandler      Duhon         Harrington    Lee           Robinson     

 

NYK      18.29    30.47 minutes 

Duhon         Gallinari     Harrington    Hughes        Lee       

 

   NYK      52.72    17.07 minutes  

Douglas       Gallinari     Harrington    Hughes        Jeffries      

 

NYK       2.13    15.25 minutes   

Chandler      Douglas       Duhon         Gallinari     Lee          

 

NYK       6.75    14.97 minutes   

Duhon         Gallinari     Hill          Hughes        Lee          

 

 

NYK      10.01 13.76 minutes   

Chandler      Douglas       Harrington    Hughes        Jeffries 

   

NYK  68.16 13.19 minutes

 

Chandler      Duhon         Gallinari     Harrington    Hughes 

     

 

NYK      48.88      11.28 minutes  

 Duhon         Harrington    Hughes        Jeffries      Lee          

 

NYK       7.76    11.25 minutes   

Chandler      Duhon         Harrington    Hughes        Jeffries 

   

NYK      11.18   10.77 minutes   

Duhon         Gallinari     Harrington    Lee           Robinson     

       

NYK      -3.60      10.08 minutes   

Douglas       Harrington    Hill          Jeffries      Robinson      25.96 years       8548_

November 25, 2009

NFL Predictions November 26

Filed under: Uncategorized — wwinston @ 10:18 am

Including games of November23, here are NFL rankings. These rankings are simply based on minimizing the sum of squares errors of predicting all games, with each game given equal weight. We see the Patriots and Saints (who play next week) are virtually tied for #1 ranking. Ratings are points are above average (minus is good for defense).

Team

offense

defense

total

Cardinals

2.73

-0.86

3.58

Falcons

4.56

-0.62

5.18

Ravens

3.01

-4.94

7.95

Bills

-6.93

-0.09

-6.84

Panthers

-2.09

0.78

-2.87

Bears

-1.54

0.96

-2.51

Bengals

1.03

-3.66

4.69

Browns

-9.06

4.34

-13.39

Cowboys

0.75

-3.34

4.10

Broncos

-2.97

-3.08

0.11

Lions

-4.16

8.56

-12.71

Packers

2.93

0.28

2.65

Texans

1.94

0.25

1.69

Colts

5.08

-6.41

11.48

Jaguars

-2.50

3.10

-5.61

Chiefs

-3.96

3.61

-7.57

Dolphins

4.11

0.32

3.79

Vikings

7.08

-0.11

7.19

Patriots

8.60

-5.41

14.01

Saints

13.78

-0.18

13.96

Giants

4.72

1.42

3.30

Jets

-0.21

-4.95

4.74

Raiders

-10.72

0.59

-11.31

Eagles

4.19

-0.64

4.83

Steelers

0.21

-2.69

2.90

Rams

-10.56

4.09

-14.65

Chargers

6.03

0.35

5.68

49ers

-1.30

-1.73

0.43

Seahawks

-2.40

1.81

-4.21

Buccaneers

-4.46

5.73

-10.20

Titans

0.83

4.97

-4.14

Redskins

-8.71

-2.46

-6.25

 

 

Here is each team’s projected final record. We used the Palisades Excel-add in @RISK to play out the rest of the season 10,000 times. Based on our simulation  we predict the following teams to make the playoffs

NFC: Eagles, Cowboys, Vikings, Saints, Cardinals, with Giants, Packers or Falcons sneaking in for last wild card spot.

AFC: Colts, Patriots, Bengals, Chargers, Ravens, with Steelers, Broncos or Dolphins sneaking in for last wild card spot.

Team

W

L

Cardinals

11

5

Falcons

9

7

Ravens

10

6

Bills

4

12

Panthers

6

10

Bears

7

9

Bengals

11

5

Browns

3

13

Cowboys

10

6

Broncos

9

7

Lions

3

13

Packers

9

7

Texans

8

8

Colts

15

1

Jaguars

8

8

Chiefs

5

11

Dolphins

9

7

Vikings

13

3

Patriots

12

4

Saints

15

1

Giants

9

7

Jets

8

8

Raiders

4

12

Eagles

10

6

Steelers

9

7

Rams

2

14

Chargers

11

5

49ers

8

8

Seahawks

6

10

Buccaneers

2

14

Titans

6

10

Redskins

5

11

 

 

 

 

November 24, 2009

The Significance of Small Samples

Filed under: Uncategorized — wwinston @ 10:50 am

 Warning: This is a fairly technical post.  You can ignore the math and skip to last paragraph (in bold)  if you wish.

 My posts often get criticized for drawing inferences from small samples. Small samples can often yield significant results. Suppose Drug A is given to 6 patients with stage 4 cancer and 5 of 6 patients survive 5 years or more.  Suppose Drug B is given to 6 patients whose cancer has advanced to a similar stage and only 2 survive 5 years or more. What is the chance (based on this small sample) that Drug A is better than Drug B? If you know statistics you can show that there is a 94% chance that Drug A is better based only on these 12 patients.

   Let’s now return to a less important topic: Basketball. Suppose player X was injured for 7 games and while he was out our best estimate is that his team (after adjusting for strength of opponents and home court) played 0.08 points better than NBA average. In the ten games before his injury and the ten games after his injury our best estimate is that his team played 6.8 points worse than average. Based on this data let’s test the following hypotheses

Null: When Player X was injured  the team played worse or the same as they did when Player X was not injured

Alternative: Team played better with player X injured.

The standard deviation of a team’s performance is 12 points per game. Cranking through the math we reject the null hypothesis with a p-value of  around .01.

Player X is Kevin Durant and the team is the 2008-2009 OKC Thunder.  So we have shown that there is less than 1 chance in 100 that the Thunder played worse when KD was out. Of course, there could be other explanations about why the Thunder improved when KD  was injured. But they played poorly before he was injured and played poorly after he returned. Is not the most logical explanation that the team played better last year when  KD was out?

   Of course, this year KD is playing much better. But it is hard to make a case that he improved the Thunder’s performance during the 2008-2009 season.

November 23, 2009

Are the Heat Hot Enough?

Filed under: Uncategorized — wwinston @ 11:33 am

At this writing the Heat’s 8-5 record ranks 6th in the Eastern Conference. After adjusting for schedule strength, however, the Heat have played 1.4 points below average, so this record is deceptive. Of course, the key to the Heat’s success is the amazing DWade. After adjusting for who he plays with and against ,we estimate that so far Wade has played 26 points per game (#3 in league) better than an average NBA player. We also have Wade as the best offensive player in the league. Wade’s great rating is consistent with the fact that when Wade is in the Heat play 7 points better than average and when Wade is out the Heat play 20 points worse than average.

     How can the Heat improve? A real key to the Heat’s success (or lack therefof)  is problems at the backup guard position. When Chalmers is in with Wade, the Heat play 14 points better than average. When Wade is in with Arroyo, however, the Heat play an abysmal 18 points worse than average.  Better backup guard play would surely lead to a few more wins.

Also as seen below, the starting lineup has played only 3 points better than average, and the two lineups in bold have been superior to the starting lineup, and probably deserve more minutes.

   MIA       2.90   158.70 minutes   Beasley       Chalmers      O’Neal        Richardson    Wade              
MIA      11.93    67.72 minutes   
Chalmers      Haslem        O’Neal        Richardson    Wade         

MIA      15.55 34.99 minutes   Beasley       Chalmers      Jones         O’Neal        Wade

November 22, 2009

Can the Bulls Play Better?

Filed under: Uncategorized — wwinston @ 11:01 am

So far the Bulls have a .500 record and (after adjusting for schedule strength)  are playing at an average NBA level. It looks like this level of performance puts them on the bubble for making the playoffs, so it is imperative for the Bulls to up their level of play. So what can they do?

     Most fans think Noah is having a great year. He leads the league in rebounding. Deng is shooting 36% on jump shots, so most fans probably think he is playing poorly. We will soon see Deng is having a great year and Noah hurts the team’s performance.

NOTE ALL NUMBERS BELOW ARE per 48 minutes and are ADJUSTED FOR STRENGTH OF OPPONENTS.

       When Deng is in the game, the Bulls play 4 points better than average. When Deng is out the Bulls play 14 points worse than average. After adjusting for who he plays with and against we find that so far Deng has been the NBA’s 2nd best player (Dirk Nowitzki is far and away #1).

     Now let’s look at Noah. Since Deng is the key we note note that with Deng and Noah in the Bulls play at an average level. But when Deng is in and Noah is out the Bulls play 14 points better than average. When Noah is in and Deng out the Bulls play 30 points worse than average. So it is not surprising that Noah has a poor Adjusted +/- rating. Here are some other key facts about Bulls lineup combos:

1. Hinrich Noah and Deng in plays 18 points worse than average. Rest of time Deng is in Bulls play 11 points better than average.

2.  Deng Miller Salmons and Noah is great: 61 mi, 20 points better than average. Rest of time Noah plays Bulls play 9 points worse than average

3. Deng Hinrich Noah and Rose without Salmon is a disaster:  In 37 minutes the Bulls play 49 points worse than average.

4. The Bulls two best lineups are Deng          Hinrich       Miller        Noah          Salmons        and Deng          Rose       Miller        Noah          Salmons  . Both lineups play 20 points better than average yet together these lineups only have played 61 minutes. The Bulls three most used lineups have played 216 minutes and average 3 points better than average.    Why not play the better lineups more?

November 21, 2009

An Open Letter to Bill Simmons

Filed under: Uncategorized — wwinston @ 9:48 am

Dear Bill:

            I am one of your millions of fans. I love your columns and podcasts. You inform  and entertain me several times a week.  We share a passionate love affair with Friday Night Lights and Mad Men. I was amazed when our school PR manager Lura Forcum informed me that you had mentioned me in your column. Since you do not seem to think much of my basketball statistical analysis I thought I should write a response to your column. So here goes.

Should Belichick have gone for the first down?

                You clearly think this was a bad decision. I think the decision is not clear cut, but math can shed a lot of light on this controversy. You quote several people on advanced topics like WIN PROBABILITY. Actually, things are not that complex. I think you agree with me that the decision depends on your estimate of three numbers.

MAKE = Chance Pats make first down

LONG = Chance Colts score TD after a punt.

SHORT = Chance Colts score TD after a failed first down attempt.

Intelligent people can differ in their estimates of these parameters. Whatever your parameter estimates, however, the math makes it pretty clear that the correct decision is to go for it if and only if

MAKE + LONG/SHORT>1.

See my post on this earlier this week. I estimate MAKE = .5, LONG = .5 and SHORT =.8 so the formula says to go for it. I polled 10 college professors on this issue and based on their estimates going for it was the right decision for 70% of the professors.

            You point out that less than 40% of two point conversions attempted with passes were successful, so you clearly think that MAKE<.50. I would argue, however, that the short field makes it easy to defend a pass on a two point conversion, so this is not a relevant data point. During the last three years, teams have converted 4th and 2 passing attempts more than 55% of the time. Clearly the Pats have a great passing game, so I think a MAKE = .50 is reasonable, but people can differ on this. Clearly, however,  Belichick’s decision to go for it was not as crazy or irrational as many people think.

            Next you point out that only around one time a year does a team score 3 TD’s in the 4th quarter and win a game. This statistic has no relevance to the question at hand. What is relevant is the chance that a team which trailed by 18-20 points in the 4th quarter that has scored 2 TD’s in a row will score a TD if they get the ball with 2 minutes left. I bet there isn’t much data on that so you have to go with your gut feel here.

            In summary, reasonable people can come down either way on the question of whether the Pats should have gone for it.

            Let’s move on to basketball. Love your book by the way. Its status as the #1 best seller is well deserved.  I totally agree with you that stats are much more useful in baseball than basketball. This is because baseball is mostly a two man game between the pitcher and hitter (fielding does matter, but it now can be well measured).  I also agree with your comments in the book that Box Score metrics like PER miss a lot of the game. I love your discussion of why we need a stat that shows that Wes Unseld was a great player(“he made his teammates better in so many ways”). While over short spans of time Adjusted +/- has a lot of noise in it, over a large sample, Adjusted +/- gets at how a player adds (or subtracts) to a team’s success.  Essentially Adjusted +/- looks at every minute of every game and uses how the score moves, together with the 10 players on the court, to tease out the influence of each player on the score of the game So now let’s look at the two assertions you made on your blog:

·        Anyone who thinks KD was a below average NBA player must be pretty stupid.

·        Anybody who thinks Tim Thomas is underrated must be pretty stupid.

As Edwards Demings, the great American statistician used to say “In God we trust, all others need data.” So let’s look at your two assertions.

How did Kevin Durant perform during his First Two Seasons?

                KD had a PER of 24 last year, which indicates to box score followers that he was a great player. I do not think this was the case. His Adjusted +/- was -7 points for his first two seasons (other people get just about the same number). This indicates that after we adjust for who KD played with and against; our best estimate is that he reduced the Thunder’s performance by 7 points a game. After factoring in the noise, there is less than a 5% chance that KD’s Adjusted +/- for his first two seasons exceeded 0 (an average NBA player).  By the way ask your good friend Daryl Morey if he thinks it is “nonsense” to say KD hurt the Thunder during his first two seasons. To substantiate the fact that KD hurt rather than helped the team during his first two years look at the following numbers (standard deviation measures the “noise”).

Let’s break down all Thunder minutes during 2008-2009 into 3 lineup combinations and look at how (adjusting for strength of opposition) the Thunder played. The standard deviation of these estimates (rounded off) is also given

·                   Collison Westbrook Green and KD in +.4 points (std dev 4 points)

·                   All other KD minutes -11.2  points (st dev 2 points)

·                   All minutes with KD out -2.6 points (std dev 3 points

I hope you can enlighten me on how these numbers show that KD helped the Thunder win a lot of games.

   By the way right before KD was injured last year the Thunder was on a 7 game losing streak. As soon as he got hurt they went 5-2. I am sure you think that was a coincidence.

            Now the good news is that KD (and the Thunder) are playing great this year. Our numbers indicate that KD is an all star caliber player this year. Could this be because Henry Abbott in his gutsy  TRUE HOOP column pointed out KD’s shortcomings (failing to play the pick and roll correctly and shooting when doubled and tripled teamed)? Maybe KD corrected these flaws in his game and this led to his improvement. If you can ask KD if this is the case, I think it might make a great story. By the way to improve a team’s performance by 20 points a game, you need only score one more basket or give up one less basket on around one out of 20 possessions (assuming 200 possessions per game).

            On to Tim Thomas. Both you and Kevin Pelton (his Basketball Prospectus is also a great book) have written that Tim Thomas “defines a replacement (read bad) player.” I do not dispute your comments that Thomas did not hustle in many Clipper games. He may be a bad off court influence; I just do not know. But in over 20,000 minutes of NBA play his adjusted +/- rating is +0.78, so he grades out over his career as a slightly above average NBA player.  This is an awful lot of data. Basically, for all of Tim Thomas’ minutes we know who he played with and against and how the score of the game changed during each of his minutes on the court. Given this data, there is virtually no chance that Tim Thomas is as bad as you say he is (against ask Daryl Morey his opinion on this). Of course, if he hustled more he would have been better.  One final stat on Tim Thomas’ performance with the Knicks last year:

In 105 minutes with Gallinari, Harrington and Thomas in the Knicks played 22 points better than an average NBA team. In 140 minutes with Gallinari and Harrington in and Thomas out the Knicks only played 3 points better than average.

I think it should clear to a basketball expert like yourself that Tim Thomas let’s you spread the floor (he also did this with the Suns) and when he is on the court with guys who can shoot and drive he can make a team awfully hard to defend. This data certainly flies in the face of your assertion that Tim Thomas is worthless.

            Well, thanks for reading this. I emailed you last year when you dissed our rating of Jason Kidd. I wanted to show you why we had him rated as a good player, but I can appreciate how busy you are. Hope to hear from you sometime (Winston@indiana.edu or 812-322-4270).

Sincerely yours,

Wayne Winston

 

November 19, 2009

Did Belichick diss his Defense?

Filed under: Uncategorized — wwinston @ 7:42 am

Many pundits believe that Belichick’s decision to go for it on 4th and 2 meant he had no confidence in his defense. As we will see, it is much more likely that the decision was driven by confidence in his offense rather than doubts about the Pats defense.

   Recall from our last post that the Pats should have gone for it if

MAKE + (LONG/SHORT)>1.

Here MAKE = Chance of getting a first down for PATS

            LONG = CHance Colts score  TD sfter punt.

            SHORT C= CHance Colts score after failed attempt at first down.

The key insight is that LONG/SHORT probably does not depend much on the quality of the PAT’s defense. For example, if the PATS defense were good we might have LONG =.3 and SHORT =.6 and if the PATS defense were bad we might have LONG = .45 and SHORT =.9. In either case LONG/SHORT would equal .5.

  On the other hand, the better the Pats offense, the higher MAKE would be, and a higher MAKE would make it more likely that the rational move is to go for it.

I think this logic shows it is much more likely that confidence in Tom Brady rather than defensive doubts drove Belichick’s decision to go for it.

November 17, 2009

Belichick: The Long and Short of It

Filed under: Uncategorized — wwinston @ 11:24 pm

My colleague Jeff Sagarin of USA TODAY fame has come up with an elegant simplification of whether or not Belichick should had gone for it on 4th down in Sunday night’s Colts-Patriots game. The decision hinges on your estimates of the following three parameters:

MAKE= chance you make first down

LONG = Chance Colts score TD if Pats punt

SHORT = Chance Colts score if Patriots fail to make 1st down after going for it.

Patriots should go for it if

MAKE + (1- MAKE)*(1-SHORT)  > 1 – LONG
                                   MAKE + 1 – SHORT – MAKE + MAKE*SHORT  > 1 – LONG
                                                        SHORT(MAKE – 1) >  -LONG
                                                                     MAKE – 1  >   -LONG/SHORT
                                                          
MAKE      >  1 – LONG/SHORT.

Thus we are indifferent between punting and going for it if MAKE = 1-LONG/SHORT.

            The following table gives some estimates of parameter values for which the decision is a “toss up.” Note if LONG exceeds value in table then Patriots should have gone for it.

Chance of Making 1st Down

LONG

SHORT

0.45

0.44

.80

0.50

0.40

.80

0.55

0.36

.80

0.60

0.32

.80

 

 

Chance of Making 1st Down

LONG

SHORT

0.45

0.385

.70

0.50

0.35

.70

0.55

0.315

.70

0.60

0.28

.70

 

I think these cases show that at worst, the decision was a tossup. I think most people thought LONG was at least 0.40 and SHORT could not exceed 0.80. My personal estimate was LONG = 0.5 and SHORT = 0.8 so Bill should have gone for it if chance of success was at least 37.5%. Put in your own estimates and  see what decision you would have made.  I think Jeff’s elegant solution gives an easy to use rule of thumb that coaches can use to inform future decision making. The main lesson to be learned from this controversy is that differences in opinion are usually due to differences in estimates of relevant  parameters, not differences in mathematical methodology.

November 16, 2009

Belichick was Right!

Filed under: Uncategorized — wwinston @ 1:17 am

Bill Belichick is one of my least favorite people in sports. I love the Colts and do not like the Patriots. The announcers were unanimous in saying the Patriots were crazy to go for it on 4th down in the Colts-Patriots game. I hate to say it, but I think Belichick’s move might have made sense. Looking back at the last two years passing plays on 4th down and 2 or less yards to go have gained at least 2  yards around 45% of the time.  With Brady this chance is probably higher than 45%. Let SHORT= chance Colts score TD from the Pats 30 and LONG  = chance Colts score TD after a punt. Assume that if Patriots get a first down Colts cannot win. Then Patriots should go for it if .45 + .55*(1-SHORT)> (1-LONG). The following table computes Pats chance of winning if they go for it minus Pats chance of winning if they punt based on different values of SHORT and LONG. A positive number means Patriots should have gone for it. Note that if Colts have a t least a 50% chance of scoring  a TD after the punt, then Bill made the right move. With Peyton Manning at the helm I would say the Colts had at least a 50% chance of scoring a TD from say 70 yards.

     

long drive

     
 

-0.03

0.2

0.3

0.4

0.5

0.6

0.7

 

0.4

-0.02

0.08

0.18

0.28

0.38

0.48

from 30

0.5

-0.075

0.025

0.125

0.225

0.325

0.425

 

0.6

-0.13

-0.03

0.07

0.17

0.27

0.37

 

0.7

-0.185

-0.085

0.015

0.115

0.215

0.315

 

0.8

-0.24

-0.14

-0.04

0.06

0.16

0.26

 

0.9

-0.295

-0.195

-0.095

0.005

0.105

0.205

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