Saturday, July 27, 2013

New blog launched devoted to sports picks

I've decided to keep this blog entirely devoted to sports and statistics, and have now launched Probabilis Sports Picks where I'll post my daily picks.

Tuesday, July 23, 2013

The NL West is mediocre: and yet is actually overperforming

The NL West is currently the weakest division in baseball, with the Dodgers leading the division with a record of 51-47, only 4 games above .500. The worst part about the current state of things in the West is that 4 of its 5 teams are actually overperforming their expected records. I've reached this conclusion based on the Pythagorean Expectation (originally derived by Bill James) for each team, calculated from their run differentials. If each team was playing to their expected winning percentage, the standings would currently look like this:

1.  Arizona Diamondbacks  50-49  .505 (+1)
T2. Colorado Rockies      50-50  .500 (-2)
T2. Los Angeles Dodgers   49-49  .500 (+2)
4.  San Francisco Giants  44-54  .449 (+1)
5.  San Diego Padres      43-57  .430 (+1)

As depicted above, the Rockies have underperformed by 2 wins, while each of the other 4 teams have slightly overperformed their expected records. As a whole, the division has won 3 more games than they should have, and still achieved their current level of mediocrity. If things had played out strictly by the numbers to date, the Diamondbacks would be in first place and primed for the lone playoff spot out of the division with a record 1 game over .500.

Monday, July 15, 2013

Adjusted conference rankings for the 2013 college football season

The latest round of conference realignment is in full effect, and so my roommate at UNC used Jeff Sagarin's NCAAF ratings from 2012 to calculate the new conference standings (using the "central mean" technique) for this upcoming season. Here is his ESPN page for citation (literally his only presence on the Internet). Let's take a look (2012 rank in parentheses):

2013 NCAAF Conference Rankings
1. SEC (1)
2. Big 12 (2)
3. Pac 12 (3)
4. Big 10 (4)
5. ACC (6)
6. AAC (formerly Big East) (5)
7. MWC (11)
8. MAC (9)
9. Sun Belt (8)
10. C-USA (10)

Keep in mind that the WAC no longer exists; they were ranked 7th at the end of last season.

Sunday, July 14, 2013

How crowd sourcing on STFC picks college basketball spreads well enough to turn a profit

When I analyzed the 2013 picks on Streak for the Cash, I not only categorized picks by sport and league, but also took game picks from those categories and split them again into the two most common gambling options: straight up (SU) and against the spread (ATS). I was mostly interested in picks against the spread, since any advantage that picks correctly more than 52.38% of the time will beat the typical Vegas line of -110. All categories against the spread actually did worse than 50%, except one: college basketball. The overall record for these picks was 98-81 (54.75%), well above the expected result of 50% and also above the critical 52.38% threshold. As before, the more confident the pick, the better the results: sides with 75-100% of picks went 72-30, winning a whopping 70.59% of the time.

It should be noted that the data I collected was only from January 1 on, so it only includes half of the season. However, had you placed a $110 bet on every consensus NCAAB ATS pick on STFC from January 1 to the end of the season, you would have made a $890 profit- a 4.52% return. Had you chosen to limit your bets to the more confident 75-100% consensus picks, you would have made a $3900 profit- a massive 34.76% return.

STFC and "sheep" pickers: why going chalk is actually the best strategy for most wins in a month

On Streak for the Cash, many players consider following the "sheep" pickers (those blindly following the favorite with the majority of the picks) a bad strategy. But are favorites really over favored by STFC players? This premise depends upon the reasoning behind what percentage should back each side of a prop: should the percentages accurately reflect the probability of each respective side winning, or should the larger percentage simply take the favorite? I.E. If the favorite wins 70% of the time, should 70% of picks back the favorite? Since the goal of STFC is to simply pick winners (and you don't have to consider value or losing money), the best strategy is to maximize your expected number of wins. With this in mind, the "sheep" are actually playing the ideal strategy: even if one side's chances are slightly above 50%, they're the better pick.

This theory is backed by the numbers: I analyzed 6,799 STFC picks between 2010 and 2011, and the favorite won 54.54% of the time. When I broke down these picks by sport/league, only in one instance did the favorite have a losing record: 9-13 (40.91%) in Auto Racing. In every other category, the favorite "sheep" pick won more often than the underdog. I also analyzed 1,649 picks from 2013, and this trend continued: 52.82% of the 2013 favorites have been winners. And the more confident the pick, the better the result: for all 2013 props with 75-100% of the picks backing one side, 56.22% of these favorites have won, compared to sides with 60-75% of the picks (46.29% correct) and 50-60% (47.09% correct).

Keep in mind that this strategy is ideal for picking most wins in a month, but not necessarily for getting a "streak". The premise behind Streak for the Cash is that each prop is close to 50/50, and  you need 27 wins in a row to win the grand prize "stash". Even if you are able to find an advantage of picking the favorite that has a 55% advantage every time, the chances of getting 27 correct picks in a row are .0000097%. If each prop is truly 50%, this falls to .00000075%. Basically, you have to get extremely lucky no matter what "strategy" you employ. However, when the goal is to get the most wins in a month, you want to maximize your expected number of wins: and picking the favorite every time is the way to go.

Numbers Numbers Numbers!!!

I watch sports. A lot. My roommate at UNC and I have determined that, when we combine the screen time of our two TVs, we watch a total of 60 hours of sports in a typical week. So, I figured I should be generating some sort of output from this: and thus this blog was born. 

I intend to write about statistics and probability, primarily concerning sports and perhaps the stock market and other topics that interest me (Carolina basketball will definitely be a focus when the season starts in November). I'm also currently refining a "betting system" I've been working on for any sport I have a model for: from MLB to NFL to NBA to even WNBA, and I'll hopefully start posting daily picks if my methods are successful.

My main influences have been Nate Silver, Ken Pomeroy, and Jeff Sagarin; their work is fantastic. Finally, regarding probabilities and picks, as a great mathematician at UNC once told me, "You can never be 100% certain!"