Like a Scotch drinker, I’ve found my taste for baseball projections has matured over the years. Where my initial taste was weaned on Dewar’s-quality projections like ESPN, Yahoo!, or some $4.95 magazine off the newsstand, I now hold out for premium, single-malt varieties like PECOTA/Baseball Prospectus and Ron Shandler. I recommend buying both of their projections online as you can get their projections in spreadsheet form.
While peers suggest I try other high-quality (and free) projections like CHONE, ZiPs, etc., I’ve put projection experimenting on hold to tackle a greater quest – one that could benefit our site’s loyal readers and the fortunate souls who get redirected here by a search engine.
The challenge is answering the question “How do you convert player projections into rankings?” As once you’ve settled on your projections, there are several key pre-draft considerations that need to take place to ensure success:
- Value of Player Based on Position Depth – e.g, how much does a player’s value increase/decrease based on the other available options for that position?
- Value of Players in Different Positions – e.g., how much do you sacrifice on a player’s total stats because they play 2B vs. 1B
- Value of a Player’s Stat Mix – e.g., how do you compare the value of 40/120/10 (HR/RBI/SB) vs. 15/75/40?
- Value of Hitter vs Pitcher Stats – e.g., how do you compare A-Rod vs. Santana?
- Value of a Player By You vs. Others – e.g., how long can you wait before picking a player?
(Note: Risk and health are other key considerations but they ideally should be factored into the projections – i.e., Rich Harden shouldn’t be projected at 200 IP)
While a solution for the above factors appears complex, the concept behind how to do it is rather simple: Convert all the statistics to the same metric (think money – it’s real easy to compare 10 dollars vs 15 Euro vs. 2000 Yen if you convert the Euro and Yen to dollars). This is the underlying concept behind Bill James’ Win Shares.
So what metric makes the most sense for fantasy baseball? Where real baseball success is measured in Wins, fantasy baseball success is measured in points.
Hence, “Point Shares”
Please click for our inaugural edition of Fantasy Baseball Point Shares for 2008. I’m going to refrain from a drawn-out explanation of the methodology. The important parts to understand are:
- Point shares represent the estimated impact on a team’s points by substituting a player for the average drafted player at his position on a team filled with average players. So in a 10 team league, this team would otherwise earn 5.5 points per category (55 points). Substituting one of those average pitchers (approx. Ian Snell) for Johan Santana would net an approximated gain of 7.8 points to 62.8.
- To account for a hitter’s value outside their position (The utility spot, the fact that a SS HR is worth the same as an OF HR), hitters receive 2/3 of points value based on their stats vs. the average drafted player in their position and 1/3 of points value based on the average drafted hitter.
- Since pitching positions can be filled with starters or relievers, player value was adjusted. Starting pitcher values are 75% based on average drafted starting pitcher, 25% on average drafted pitcher. Relievers are 40% on average drafted reliever, 60% on average drafted pitcher.
- Hitters are placed at their most valuable position where they are 20 games eligible. Their rank/value at other positions they are eligible (down through expected eligibility like Ryan Braun in OF) is listed lower down in the spreadsheet.
- Two versions are included: a 10 team, 5×5, MLB universe, C/1B/2B/SS/3B/CI/MI/5 OF/Util/9P and a 12 team, 5×5, MLB Universe with 2C/1B/2B/SS/3B/CI/MI/5 OF/Util/9P.
As with any player ratings system – especially one this ambitious – the standard question would be “How do you test this?”. The beauty of this methodology is it was relatively easy to test. I took 7 drafts off of Mock Draft Central and calculated the rankings based on the underlying projections (weighted model of PECOTA and Shandler) and the Point Shares.
After making a few adjustments, the results of the test were very promising – Point Shares predicted total team points within +/- 2 point for 45 of the 70 teams. Another 18 were predicted within +/- 5 points. Only 1 team fell outside of +/- 7 points.
On a category-by-category basis, the Point Shares correlate well with the total team stats. For the hitting stats, the team Point Shares correlated at 97+% with the total stats. For pitchers, Saves, ERA, and WHIP correlated at 90+% while Wins and Strikeouts were at 90% aside from one league where the projections tanked. Why did the pitching stats not do as well as the hitting stats? It is because of the random mix of starters and relievers who – unlike hitters – have vastly different counting stats. ERA and WHIP proved most successful because they could be weighted by innings pitched.
Look out for future posts referencing these Point Shares and probably make some tweaks along the way – especially if we get revised player projections.
We also want to state clearly that this is NOT a recommended draft ordering. The main reason is that it doesn’t factor in the 5th pre-draft consideration mentioned earlier – the “Value of a Player By You vs. Others”. Yes, I believe Peavy is worthy of a top 5 pick but if you can get him in the 2nd round or possibly the 3rd round, by all means wait. Average Draft Position stats are the one piece of valuable information you can get from Yahoo!, ESPN, etc. If you’re playing in an advanced league, you may want to use those on Mock Draft Central (requires subscription).
Also note that some of the differences aren’t statistically relevant. If you like Jose Reyes at 3.65 over Ryan Howard at 3.72, go with your gut b/c it’s a virtual pick’em anyway.
So use this as your rankings base then make adjustments based on your preferences and your feel for your fellow drafters. And good luck…