Check out the MLB rankings here, or click the "MLB Rankings" tab at the top of this blog.
Behind the Rankings
Behind the Rankings
I don't want to get too in depth here into the details of the mathematics behind the ratings. So, I'll keep it relatively simple in this blog, and refer you to a reference where you can dig into it more if you'd like. I use a slightly modified version of Microsoft's TrueSkill ratings system. Why slightly modified? Two reasons: 1) Microsoft wouldn't elaborate on the complex details of competitions involving three or more competitors. 2) Microsoft would give the specific equations of the 'v' and 'w' functions (check the details in the reference if you care), so I had to curve fit.
Issue #2 isn't that big of a detail, my curve fit matches extremely close to their 'v' and 'w' function plots. Issue #1 doesn't effect head-to-head competitions, which cover the vast majority of sporting events, so this does not apply to my MLB rankings. For the curious readers out there, I devised a fairly accurate way of simulating their complex methods of three or more competitor events that tracks very closely with their results. With all that said, I'm satisfied with my Matlab version of the TrueSkill Rating system.
I think we can all agree that a team isn't always as good or as bad as their record. Strength of schedule matters. A team can have some very quality wins against a strong opponent, or an embarrassing loss against a poor opponent. From a 30,000 ft view, my MLB ratings (again, based on Microsoft's TrueSkill) measures each team based on the quality of opponent they compete against by tracking two parameters for each team: average skill (mu) and a measure of uncertainty of that skill (sigma). Many rating systems only track the "skill" term. By tracking both skill and uncertainty, you can converge to a more accurate representation of a player's (or team's) skill, with a smaller sample size. A team's opponent's skill, uncertainty, and outcome of the event effect that team's recalculated skill and uncertainty. The rating is generated from subtracting three times the uncertainty from the average skill (rating = mu - 3*sigma). This results in a 99% confidence that the team's skill is at or above that rating.
More details here if you are interested! Microsoft TrueSkill Rating
I'd also be happy to answer questions if you'd rather have someone translate that for you: Post your questions via email or comments on this post and I'll get back to you.
Current Rankings
(Note: This reflection of the current rankings is based on the posted rankings as of 29 July)
Check out the MLB rankings here, or click the "MLB Rankings" tab at the top of this blog.
Tampa Bay, Pittsburgh, LA Dodgers, Boston, and Oakland are in the top 5 of my rankings as of 29 July 2013. The Dodgers have made a steady climb in the rankings in the last month. They have turned their season around remarkably. In the past 30 games, they have gone through a 7 game swing from the Arizona Diamondbacks to take the lead in the NL West. I have a pretty nifty post processing script that allows me to compare ratings and W-L record from any point that I have saved in the season. For the 29 July rankings, the deltas are calculated from the All-Star break, as noted at the top of the rankings. The Rays and Dodgers both have gone 9-2 since the break, while charging up my rankings. Pittsburgh dropped from the #1 spot to second while maintaining 6-6 record since the ASG.
A couple things to note to help you get acquainted with my rating system. Let's look at the Yankees and Diamondbacks:
| 13 | NY Yankees | ( 55- 50) | 90.33 | ( 4- 7) | -0.85 |
| 14 | Arizona | ( 54- 51) | 89.84 | ( 4- 7) | -2.07 |
Both teams went 4-7 since 13 July, but New York only dropped -0.85 points in the ratings while Arizona dropped -2.07. Why is this? Again, this reflects the quality of opponents the Yankees and Diamondbacks beat and lost to during that span. The majority of the Yankees losses since the break came to Tampa Bay (currently #1) and Boston (currently #4), while the majority of Arizona's losses came at the hands of the Giants (#27), Cubs (#16), and Padres (#19). Also note, the few wins that the Yankees and Diamondbacks racked up during those games came from the same teams mentioned above. See the difference? The Yankees are being rewarded more and punished less for their wins and losses against their stronger competition.
As another side note, it can be tough at the top. With the highest rating, a team is expected to win most of its games. This is why Pittsburgh slid a bit while going .500. Winning half your games will not keep you at the top for very long.
| 2 | Pittsburgh | ( 62- 42) | 97.96 | ( 6- 6) | -1.49 |
If you notice any errors in the W-L record as of a specific date, please let me know. I found two errors already on ESPN (which I just copy and paste into my rating system) that I tracked down.
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