Sunday, March 4, 2018

Equity Analysis: Where To Place Your Bets (WIP)


Last week I explored the common threads across league-winners -- that is, is there something repeatable and useful about what league champions are doing? One of the key questions that surfaced from the analysis was, "are there certain positions that are better bets than others?" For instance, conventional wisdom thinks pitching and catchers tend to be the most volatile positions, and thus might warrant less draft capital. Are they right? That's what I wanted to dive in deeper to here. Furthermore, why does this matter? Well, Rudy Gamble of Razzball, among others, have conducted research on years of past champion data. They found that across all champions, they tend to earn value from their top picks. That makes sense intuitively -- if you don't get production from your top picks, then, well, it's going to be hard to win. But my question then becomes, how do we know who's a good top pick to bet on? That's some of what I'd like to get into here.


To conduct this analysis, I first wanted to understand how draft value correlates with end-of-season value. I used the last three years of one of my home league's data (traditional 5x5 mixed, 12-13 teams) to assess the relationship between draft day cost, end-of-season value, and the resulting equity. While we're very interested in which positions returned the best equity, I wanted to also look into which positions tend to see the highest bust rates.


In starting this analysis, I first looked at the correlations between each position and their end-of-season value. Here were the results, sorted by highest r-squared to lowest:

Position Overall-EOS
2B 0.396
1B 0.347
DH 0.294
SP 0.243
3B 0.216
C 0.159
OF 0.150
RP 0.058
SS 0.043
Hitting 0.225
Pitching 0.224

Starting at the top, second base and first base had the strongest relationship between draft-day cost and end-of-season value. Based on this analysis, one could reasonably hypothesize that these might be good positions to invest larger dollars into. Going back to Gamble's analysis (I can't find the link -- someone please let me know if they have it!), he states league-winners see very little loss among their top picks, avoiding injury, maintaining production, etc. That isn't to say they return excess value, per se, just that they returned value aligned with their cost. That's how I'm thinking about 2B and 1B here -- if Gamble is arguing that one step to winning your league is not blowing your initial picks, then I think 2B and 1B could be great places to start. More on this later.

On the other hand, consistent with some analysis Rob Silver had been discussing, closers' draft-day cost has very little correlation with their end-of-season value, with only SS being worse. To Silver's point, I found that among 89 RPs drafted during this time frame (15-17), only 10 broke even or returned surplus value (equity). That is, only 11% of RP at least broke even. Now, that isn't to say that you should ignore closers. In fact, in last week's analysis, we found that saves correlate with overall pitching points by a 0.30 r-squared. Rather, the key is to sink as little as possible into the position while still positioning yourself for saves. When we look at RP who lost no worse than $3, the number of pitchers that qualify jumps to 22. Their average draft salary is $7, ranging anywhere from several $1 closers to a $24 Kenley Jansen in 2017. The bottom line is, draft closers at prices where you think they can break-even. The smart money says it's hard to predict who that will be, so minimize your exposure. Who will be this year's Greg Holland, Raisel Igelsias, and Fernando Rodney? On a side note, I think SS may be skewed by some horrific Troy Tulowitzki and Hanley Ramirez seasons, where they were drafted like top picks but shit the bed, largely as a result of injuries. I don't believe this is representative of the new wave of SS we're seeing in Correa, Lindor, Turner, and Seager, who were all keepers/freezes during the majority of this analysis.

Lastly, I found the fact that hitting and pitching had largely the same relationship with their respective EOS values. Pitching is often thought of as much more volatile, but in this analysis, the numbers suggest they aren't as different as we often think. Specifically, SP draft cost had a stronger relationship with EOS value than 3B, C, and OF. Speaking of C, they are often thought of as the most volatile hitting position, which is mostly consistent with what this analysis shows, even if OF and SS fall behind.


While it's interesting to note the correlation between what you pay for a position on draft day and what they finish the season as, there are additional aspects to consider, such as price, break-even rates, equity rates, and bust rates, which all might differ based on how much you pay for a particular position. 

Consistent with the correlation analysis above, it appears 2B and 1B have the lowest bust rates while C and RP have the highest. In this case, bust rate is defined as a player realizing only 10% of their draft-day cost (i.e., a player who cost $10 but only earned $1 or less would qualify as a bust in this case). More details here:

Position Analysis, All, 2015-2017
Position Drafted > B/E > $10 Equity > $20 Equity Bust
1B 69 43% 26% 9% 39%
2B 58 43% 19% 10% 36%
3B 57 33% 19% 12% 40%
C 78 24% 12% 3% 64%
OF 199 35% 18% 7% 46%
SS 56 21% 11% 5% 48%
SP 213 26% 10% 4% 48%
RP 89 11% 1% 0% 61%
Batting 517 34% 18% 7% 46%
Pitching 302 22% 8% 3% 52%
Across all positions, it seems like 1B, 2B, and 3B are most likely to break-even, return surplus value (equity), and avoid being a bust. While we should be careful about applying predictive value to this descriptive analysis, these are useful pieces of data to construct our rosters. In the last three seasons, these positions have had players that have performed better than other positions. Perhaps we can apply to this season and build our teams and larger purchases around these positions. After all, Gamble argued that league-winners top picks didn't bust. Are these the areas to invest to put ourselves in position to get value from our top picks?

Interestingly, and on the contrary from the correlation analysis above, batting and pitching as a whole look very different. While they both had virtually the same strength of relationship between their draft-day cost and EOS value, hitters really shined in this analysis, outperforming pitchers in their ability to break-even, produce meaningful equity, and avoid busting. Maybe this justifies why most of the industry is focused on hitting-to-pitching at a 67/33 rate.

Consistent with the correlation analysis above, it appears C and RP are unlikely to break-even, produce equity, and are more likelier to bust. I think these positions are best treated very thoughtfully, with lower investments warranted -- you need to think about how to profits here, but perhaps the thinking should be how can I achieve value at minimum cost, while focusing more on other more bettable positions. Only 11% of RP broke even and nearly no one returned meaningful equity for Christ sakes!

While this analysis is great, it's only so useful from this higher-level. To really understand which areas of each positions are likely to be better investments, we need to dive deeper into the data and double-click into buckets of investment. In this case, I looked at the same things above but from the perspective of differing draft costs: $1-10, $11-20, and $21+ for each position.


Going back to Rudy Gamble's point, league winners earn meaningful value from their top picks, even if at just a break-even basis. After all, if you're "hitting" on $1 Aaron Judge's and/or $1 Luis Severino's and adding those to a productive core of break-even larger investment players, you're suddenly looking at a much more dangerous team than if those Judge or Severino hits are merely making up for costly early investments. That begs the question: where should we be investing if we want to return value? I can't answer that question without being able to predict the future [insert obligatory comment of "at which point, I wouldn't be writing this"], but perhaps we can look at past data to give us some clues:

Position Analysis, $21+ Draft-Day Cost, 2015-2017
Position Total Total % > B/E > $10 Equity > $20 Equity Bust
1B 25 36% 60% 32% 16% 12%
2B 15 26% 53% 27% 13% 20%
3B 13 23% 31% 15% 15% 31%
C 4 5% 50% 25% 25% 50%
OF 50 25% 30% 16% 6% 40%
SS 10 18% 10% 0% 0% 80%
SP 53 25% 28% 13% 6% 45%
RP 2 2% 0% 0% 0% 0%
Batting 117 23% 38% 20% 10% 34%
Pitching 55 18% 27% 13% 5% 44%

In the table above, we're only looking at players that cost at least $21 on draft day. You'll notice that C and RP have particularly small samples (only 4 and 2 players drafted, respectively). Also of note, in this case, I'm calling anyone a bust who didn't return at least 50% of their value (i.e., if a player drafted for $30 doesn't return at least $15, they are deemed a bust). For what it's worth, the bust rates on a relative basis didn't materially change even as they were adjusted to, say, minimum 30% returned.

Similar to the analysis above, 1B and 2B in particular stand out as having been solid investments, seeing by far the largest break-even and equity rates, while seeing the lowest bust rates. That's exactly what you're looking for in your early purchases -- how can I build a winning foundation to enable later big "hits" the ability to carry my team as a league-winner? Clouding this analysis -- particularly on the 2B side -- is the potential freezing issue. Altuve was frozen for $20 and $25 in consecutive years, and that wasn't controlled for, leading to potentially artificially high equity rates, and artificially low bust rates. Even so, had Altuve been drafted at market rates, he still likely wouldn't have negatively impacted these numbers. Maybe there's something about this recent crop of 1B and 2B that makes them more reliable. 1B has traditionally been known as a hitter's position, so it's not particularly surprising to see them as more reliable here. On the other hand, I was bit more surprised at 2B.

Looking at the other end of the spectrum, despite the extremely small sample sizes, C and RP perform as expected -- C looks like a 50/50 shot at these prices to return value or bust, while neither of the two RPs drafted at these rates returned value or busted. So it goes. I don't want to invest at these levels at either position, I'll look to other positions at these prices, and pounce on the best values at these positions. SS also shows up extremely poorly here, with 8 of 10 players drafted qualifying as busts. Again, I'm hesitant to apply this thought process to the 2018 wave of SS , but something to file away.

In between, looking at the two most drafted positions in this range, OF and SP appear to be dangerous positions here. Only 30% and 28% break-even, while 40% and 45% bust for OF and SP, respectively. Let's see if this improves at lower costs.


The $11-20 bucket represents an interesting strategy -- it's a material investment, but only 4-6% of budget. This feels like a great place to make strategic bets on upside. The bust rates are unsurprisingly higher, while the equity rates -- specifically for batters -- aren't that far off from the previous higher spend group.

Position Analysis, $11-20 Draft-Day Cost, 2015-2017
Position Total Total % > B/E > $10 Equity > $20 Equity Bust
1B 24 35% 38% 25% 4% 46%
2B 14 24% 36% 21% 7% 36%
3B 22 39% 41% 27% 14% 45%
C 28 36% 25% 18% 4% 54%
OF 67 34% 40% 21% 10% 43%
SS 23 41% 17% 0% 0% 48%
SP 65 31% 26% 11% 3% 55%
RP 46 52% 4% 0% 0% 61%
Batting 178 34% 34% 19% 7% 46%
Pitching 111 37% 17% 6% 2% 58%
Position Analysis, $1-10 Draft-Day Cost, 2015-2017
Position Total Total % > B/E > $10 Equity > $20 Equity Bust
1B 20 29% 30% 20% 5% 70%
2B 29 50% 41% 14% 10% 55%
3B 23 40% 26% 13% 9% 57%
C 46 59% 22% 7% 0% 76%
OF 85 43% 33% 16% 5% 61%
SS 23 41% 30% 26% 13% 61%
SP 97 46% 24% 8% 3% 64%
RP 41 46% 20% 2% 0% 71%
Batting 226 44% 31% 15% 6% 64%
Pitching 138 46% 22% 7% 2% 66%


Everything fluid and dependent on projected values and draft-day cost, but if were drafting based on the last three years:

Best Investments
1B, 2B, 3B
Would be great if they were in their prime (26-30) and on good teams
Take Your Shots

Minimize Loss of Capturing Production

Aaron Sauceda Web Developer

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