Will Scoring Decrease Under New NFL Kickoff Rule?

football-kickoffIn an attempt to reduce player injury, the National Football League has modified their kickoff rule for the 2011 NFL season.  Kickoffs, which occur after scores and at the beginning of each half, will now be taken from a team’s 35 yard line.  Moving the kickoff five yards downfield may seem like a minor change, but significant consequences have already been seen in pre-season games.

As expected, the number of “touchbacks”, or un-returned kickoffs, has dramatically increased.  The kickoff return is an exciting play in football, and decreasing the number of returns has some fans and observers worried that the games won’t be as fun to watch.  As a football fan, I see this as a legitimate issue, but as a quantitative-minded person, my first thought was “How will this new rule affect scoring?”.

I looked at AdvancedNFLStats.com, an excellent collection of unique quantitative analysis by Brian Burke.  In a post examining the value of a touchback, Burke creates a graph of expected points by 1st down field position.  For example, the ordered pair (60, 1.2) on the graph represents the following piece of information:  teams starting on their own 40-yard line will score about 1.2 points per possession on average.

It’s no surprise that the graphs displays a negative correlation:  as distance from goal increases, expected points decrease.  But it is somewhat surprising how linear the relationship looks.  A rough estimate suggests that the slope of the line is about -1 / 20;  this means that for every additional 20 yards of distance from the goal, the team will, on average, score one less point per possession.

This is where the new kickoff rule comes in.  More touchbacks means more drives starting on the 20 yard line, and this will likely decrease the average starting field position for both teams.  More yards to goal means less points scored per possession.  The question now is, how much less?

Well, the average starting field position after a kickoff around the 27 yard line.  In the 2011 pre-season, 34% of kickoffs were touchbacks, up from 16% last year.  A touchback results in a team starting at the 20 yard line.

If the overall average starting position is the 27 yard line, we can use the following equation to find the average starting field position for a non-touchback kickoff:

(0.16)(20) + (0.84) (X) = 27

Solving for X gives us the average starting field position after a non-touchback kickoff, which is around the 28.33 yard line.

Let’s assume that non-touchback kickoffs will be unaffected by the rule change and so, on average, will start at around the 28.33 yard line.  If the touchback percentage for the regular season rises to 34%, this means that the new average starting field position after a kickoff will be

(0.34)(20) + (0.66) (28.33)

This comes out to around 25.5.  This suggests that the new rule will cost each offense, on average, about 1.5 yards of field position per kickoff.  With around ten kickoffs per game, this translates into 15 total yards of lost field position, and that equals about 0.75 total points lost per game.

It’s possible that the rule change could affect non-touchback kickoff returns as well, as the coverage team may now have a greater advantage.  A reduction in non-kickoff returns by, say, 3 yards, would mean the overall average starting field position after a kickoff would be

(0.34)(20) + (0.66) (25.33) = 23.5

That is, around the 23.5 yard line.  This could translate into almost two points less per game!

This is an admittedly rudimentary analysis, but it’s an easy way to start a quantitative investigation of this rule change.  And it will make watching the scores this year even more fun!

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NYT Presents: Jeterography

In celebration of Derek Jeter’s 3,000th major league hit, the New York Times ran a really cool infographic displaying the relative frequencies of where those 3,000 hits went.

http://archive.nytimes.com/www.nytimes.com/interactive/2011/06/15/sports/baseball/jeter-3000-hits.html

In addition to tracking where all of Jeter’s hits have gone, two graphics also compare his first 1,500 hits to his second 1,500 hits:  not surprisingly, the second 1,500 consists of fewer home runs and fewer hits to left field.  A similar graphic shows Alex Rodriguez’s hit patterns.

There are several other interesting data displays here, including year-by-year histograms for all 28 MLB players with 3,000 hits.

Analysis of NBA Finances

This is a comprehensive and insightful look into the NBA’s claims of financial distress from Nate Silver:

https://fivethirtyeight.blogs.nytimes.com/2011/07/05/calling-foul-on-n-b-a-s-claims-of-financial-distress/

As the NBA prepares to battle the player’s union over revenue, the league has made several public claims about how they have been losing money for years.

Silver takes a deep look into those claims.  He crunches the numbers and compares player revenue as a share of league revenue across the four major sports leagues; he looks at salary growth relative to league growth; and he also discusses some of the dubious accounting tricks teams and leagues use to make profits disappear!

As usual from Nate Silver, this is a very interesting and readable application of mathematics and statistics.  His conclusion is summed up best by a recent message from @fivethirtyeight on Twitter:  “If David Stern really thinks the NBA lost $370 million last season, shouldn’t he have fired himself?”

NBA Draft Math: Evaluating Team Success

After developing a simple metric for evaluating the success of NBA draft selections, I used that metric to investigate talent dispersion in the draft and then to compare the strength of various “draft classes”.  As a third application of this metric, I will now analyze the success each NBA teamin making their draft picks.

I am using the total number of minutes played in the first two years of a player’s career as the basic quantification of draft pick value (the reasoning for this is explained in detail in NBA Draft Math, Part I).

In order to rate the success of a team, I looked at how each team’s draft pick performed relative to the average player chosen at that draft position for the NBA drafts between 2000 and 2009.  I then computed the percentage difference between that team’s choice and the average player at that pick.  The team’s overall rating is then the average of the percent differences for every draft pick.

The chart below summarizes the analysis for the Philadelphia 76ers.

As you can see, all but one of the 76ers draft choices performed better than the average player selected at that draft position.  Overall, draft picks selected by Philadelphia performed about 34% better than average; they topped the list in this ranking.

This chart displays all NBA teams whose picks performed better than average.  In addition to the 76ers, teams that performed notably well by this measure were the San Antonio Spurs, the Houston Rockets, and (surprisingly?) the New York Knicks.

The teams that performed worst in the analysis were the Boston Celtics, the Portland Trailblazers, and the Charlotte Bobcats.

Some interesting results!  The basic limitations of this metric have been addressed in NBA Draft Math, Part I, but this simple approach has opened up a lot of opportunities for analysis, and naturally, improvement.

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Rating the Ballparks

baseball-stadium-rankingsIn an attempt to rate the various Major League Baseball stadiums around the country, Nate Silver looked at the user ratings from online review site Yelp.  Noting that every ballpark has at least several hundred user reviews, Silver compiled the data from Yelp’s 1 to 5 rating system to create an ordering of the stadiums.  Once complete, the list creates a natural starting point to investigate questions like “Is ballpark satisfaction correlated with team performance?” and “How valuable is a retractable-roof stadium?”

Silver also provides the standard deviation for the ratings for each ballpark and explains the significance.  Standard deviation is a measure of the dispersion of data, so a higher deviation means more extreme ratings.

A great, fun little project!  What else can we rate using available user ratings?

Read the full article here.

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