Can Alex Smith Save the 49ers?

I recently broke the news to my son that Alex Smith appears to be slotted in as the starting QB for his beloved 49ers. I took the dejected look on his face as a challenge to provide him some hope. Going to the data on QB's is always a iffy proposition as QB stats are the product of the efforts of an entire team, not just one player and given the woeful play of the 49ers over the last few years, I was expecting that this would be a rather fruitless effort in restoring hope.

I pulled up Smith's page on Pro-Football Reference and checked on his performance. I then took his age, years in the league, Yds/Att and interception rate for last season and plugged them into PFR's useful Player Season Finder. I frankly did not believe what I found. Besides Smith, the Player Season Finder found three other players who had a similar performance in Yds/Att and interception rate during their 5th season, at age 26: Drew Brees, Peyton Manning, and Ben Roethlisberger. Looking at a broader range of stats, Smith actually fits in with this group fairly well.

On Yds/Att, QB rating, TD% (indexed by PFR), Int% (indexed by PFR), and Completion Percentage, Smith fits right in. He is better than Big Ben in TD%, QB rating, and Compl%, better than Brees in Int%, and just behind the three on Yds/Att. The other three of course all of Super Bowl rings and multiple All-Star game appearances on their resumes. So what is going on? How can the much maligned Alex Smith, playing for multiple head coaches and offensive coordinators, have delivered a similar performance as these shining examples of QB play?

To try and answer that question, I expanded my search. I removed all the filters on performance, and just looked for QB's in their 5th season while they were 26 years old. Since 1980, there are 33 QB's not named Alex Smith who played their 5th season when they were 26. After eliminating all the QB's with fewer than 8 games played, I loaded their advanced passing stats into two different clustering analyses to find groupings of players that had similar performances across all of the stats. The results of the two analysis were the essentially the same and put Smith in with the following group:

1. Tommy Kramer
2. Steve McNair 
3. Rich Gannon       
4. Bernie Kosar       
5. Neil Lomax         
6. Gary Hogeboom      
7. Trent Dilfer       
8. David Whitehurst   
9. Randall Cunningham 
10. Paul McDonald      
11. Kyle Boller        
12. Tim Couch          
13. Ben Roethlisberger

So this list seems rather more satisfying. Some players who have had success (Gannon, McNair, Boller, Roethlisberger) and some that haven't (Hogeboom, Couch). For the most part this list should provide 49er fans with some hope. There are no spectacular QB's here, but plenty that have played good efficient football and several that achieved more later in their careers (Gannon, Cunningham, McNair).

For some context, the rest of the QB's fall into the following groups:

Stars Flashes No Impact Dan Marino Vince Evans         Dave M. Brown       Peyton Manning      Mike McMahon        Mike Moroski        Chris Miller        Drew Brees          Dave Krieg          Brett Favre         Daunte Culpepper    Aaron Rodgers       Ok, maybe Chris Miller is not really a star, but for the most part, these groupings seem fairly reasonable, so 49er fans, have some hope, Alex Smith may just be Trent Dilfer.

Initial Poll Results

As data starts to arrive from the entirely unscientific poll posted below, one result jumps out immediately. The very first question of the poll asks which area within analytics will have the biggest impact on teams. The choices offered were: Collecting new data, better data management, new metrics, integration of statistical analysis in decision making, and better information systems for decision makers. In writing that question I had imagined that the "new metrics" answer would be fairly popular. That is the area that draws most practitioners initially, as it is the use of statistical tools to measure and learn about a sport. The results did not at all match that expectation.

The chart above indicates that integration of analysis and better information systems are the areas that the respondents felt would have the biggest impact, while none of them selected new metrics. What this result suggests (and again, this was hardly a rigorous poll) is that the area of analytics that is perceived to be the biggest area of growth is finding ways to help decision makers use the analysis that has already been done. This can take the form of better reporting, more in depth conversation, better explanations, and/or better information systems (that put the decision makers in control of the analysis to some degree), to name a few.

Still time to take the poll if you have not already.