Top Ten: 2013

It’s been an exciting year at Sports + Numbers, with several posts that went beyond my normal several dozen readers into several hundred or thousand. As you can see in the top ten list below (in descending order to the number one post by pageviews) this was a particularly good year for football posts. I encourage you to look around and see if there is something new to you among these. Readership has grown steadily throughout the year so some of my favorites (8, 9 and 10 particularly) may have been old news by the time you found the blog.

Over the next few days/weeks I am working on another interesting NFL-related post about the effectiveness of signing free agents so keep an eye out for that. Until then, enjoy the greatest hits from 2013:

10. Some news is worse than no news

Another strong post from NFL Draft season. Using Mel Kiper’s rankings immediately before and immediately after the scouting combine, an analysis of whether the additional information of the combine actually makes predictions sharper on a position-by-position basis.

9. Luck vs skill in NFL draft performance 

The first of a series I did searching for evidence of above-average performance in drafting attributable to skill rather than luck. This post looked at year-over-year performance of teams while subsequent posts examined individual team performance within a single draft and finally any evidence of outperformance among players selected after a team traded up.

8. On the injury rates of running QBs 

Using team-reported injury data (Probable, Questionable, Doubtful, Out) this analysis looks at whether the week-to-week change in level of injury is correlated with the number of QB rushes and sacks.

7. Sports Gini: Inequality within major sports 

The first of a series of posts I did over the summer looking at the impact of income inequality at a sport level (teams vs other teams) and then at a team level (players vs other players) to see if inequality helps or hurts a team – effectively looking at whether it’s better to have a star and a bunch of nobodies or a broader core of “good” players.

6. What's the matter with Win Probability?

Examination of the year-to-date performance of Win Probability in assessing actual probability to win games. Shortly after this (but sadly not because of this) Brian Burke at advancednflstats.com introduced some updates to the model that render some of this irrelevant. 

5. NFL draft value charts for everyone! 

A quick summary of the differences between the classic Jimmy Johnson draft value chart and some of the other offerings out there including my own.


4. Faster 3 & outs for everyone: Pace of play in the NFL 

Look at how fast teams are playing (faster) and whether faster teams actually have better offenses (not really).

3. What are NFL draft picks worth (and do they help teams win)?

This one is cheating a bit, since I first published it in November 2012. That said it was my third most popular post this year so I’m including it anyway. This was intended as a book chapter and the (slightly) higher quality really shows (formatted graphs! attempt at narrative structure! spell check!). Check this out for all you ever wanted to know about the NFL Draft and probably some more beyond that.

2. NFL draft trade evaluations

Purely by-the-book analysis of pick for pick trades in the 2013 draft. Posted after Day 1, Day 2 and Day 3 of the annual selection meeting.

1. NFL draft trade machine

The biggest hit by far in a good year for the blog. Not necessarily the most exciting from an analytical point of view, this post does a nice job making my draft value chart (and others) actionable by bringing in a bit of Excel expertise from my day job to make a trade evaluation tool.

Player performance curves and value for money

As mentioned in this space recently, I am the proud owner of a shiny new database full of player performance and salary data from 2003 to 2009. I will be trying to extend it in both directions as I have time. For now, however, the analysis will be applicable to that period’s decisions. Once 2010-2012 are added in it might provide a nice contrast in allocation and relative performance under the conditions of the new CBA.

The jumping off point for this data set is getting a good baseline on the efficiency of spending in the NFL. How much does it cost to squeeze one unit of Approximate Value[1] out of a given position? Approximate Value is a stat from Pro-Football-Reference.com developed by Doug Drinen that works by allocating out a team’s offensive and defensive performance to different positions based on various assumptions. The summaries Doug has produced introducing the stat are extremely helpful, but you won’t be at too much of a disadvantage if you just read on without understanding exactly how AV works. As he says in the introduction they are “simple, intuitive and approximate."

Methodology
  
In the post I wrote about spending on running backs (see here for more) I noted that prior to the current, 2011, collective bargaining agreement, teams frequently failed to spend up to the level allowed under the salary cap. To correct for this, I represented allocation decisions in terms of percentage of team spending. This way you have a team like the 2005 Seattle Seahawks who spent $67 million against the cap give or take a few while they were permitted to get up to $85.5 million. The $0.75 million cap number for Isaiah Kacyvenski – a fine linebacker and fellow 2011 Harvard Business School grad – would be 0.9% of the salary cap but the 1.1% of team spending is a better representation of the allocation decision. The assumption here is that teams were working under a budget set externally (owner, rather than salary cap) and that they had to allocate those scarce dollars according to that budget. If you still have a problem with this approach please do check out that article I referenced earlier.