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Last week, I did a piece on the return from \$1 players and several people asked for different breakdowns as an extension of that work. In response to those requests, I went back and pulled the draft day dollar values from both the AL and the NL Tout Wars drafts from the past two years as well as those players’ final season values and calculated the differences. Here are those findings involving those 1,149 players drafted between the four leagues.

Who makes up the drafted player pool?

 \$ Range Total Players % of Drafted 1-4 376 33% 5-8 158 14% 9-12 150 13% 13-16 149 13% 17-20 118 10% 21-24 87 7% 25-28 48 4% 29-32 30 3% 33+ 33 3%

How many players in each bracket turned a profit of at least one dollar?

 \$ Range % Profitable % Losses 1-4 38% 62% 5-8 37% 63% 9-12 37% 63% 13-16 34% 66% 17-20 32% 68% 21-24 30% 70% 25-28 29% 71% 29-32 23% 77% 33+ 25% 75%

That table shows what should be expected: the more money you spend on an asset, the less likely it is to turn a profit. After all, that is what buy-low/sell-high is all about. If we were to step out groups by ten, the results are more impactful:

 \$ Range % Profitable % Losses 1-10 38% 62% 11-20 33% 67% 20-29 29% 71% 30+ 10% 90%

If you are looking for a sweet spot in an auction, the dollar amount range with the highest return on investment with a large sample size are players drafted at \$9 or \$10. Their percentage of profit was four percentage points higher than any other dollar amount.  The largest player pool of profits remains in the \$1-\$2 range as eight percent of all profitable players from the four drafts were rostered at those dollar amounts.

Nobody drafts \$30+ players looking for profit; those are the roster spots where you are banking on production and getting what you pay for. When those players turn in losses, it is likely due to injury or serious regression that was more dramatic than the prognosticators predicted it would be.

How does it break down by position?

 Total % Profitable % Losses SP 293 26% 74% OF 270 40% 60% 156 45% 55% C 101 25% 75% 90 29% 71% SS 84 49% 51% 3B 76 25% 75% 73 37% 63% DH 6 100% 0%

Let’s all run out and draft 23 designated hitters! Based on the data over the last two seasons, there is an argument to be made to avoid heavily investing in starting pitching given its very low returns on investment. The catching position also suffered similar losses despite the fact that many like to spend very low dollar amounts on one or both positions.

Mean profits and losses by drafted value range?

 \$ Range Mean Profit Mean Loss 1-2 \$5.71 -\$3.64 3-4 \$7.13 -\$4.42 5-6 \$5.38 -\$4.98 7-8 \$6.30 -\$6.11 9-10 \$6.32 -\$5.94 11-12 \$6.36 -\$6.96 13-14 \$8.09 -\$7.98 15-16 \$6.82 -\$8.20 17-18 \$6.81 -\$7.90 19-20 \$7.12 -\$7.45 21-22 \$7.42 -\$8.17 23-24 \$6.43 -\$8.54 25-26 \$4.29 -\$9.48 27-28 \$5.29 -\$10.11 29-30 \$7.00 -\$11.15 31-32 \$8.00 -\$8.71 33-34 \$12.00 -\$10.71

By this metric, the sweet spot falls a few dollars higher, but another sweet spot in the draft comes around \$20. Then we're begged the question, "Is it worth drafting a \$40 Albert Pujols that has a low chance of returning a profit (but perhaps a greater profit if he does return one), or do you spread the risk with two \$20 players that have a higher percentage of returning a profit in the first place?" There is a case to be made for both plans of attack.

Mean profits and losses by position?

 Mean Profit Mean Loss SP \$5.04 -\$6.75 OF \$8.54 -\$6.20 \$6.69 -\$5.13 C \$5.36 -\$4.50 \$6.88 -\$7.90 SS \$5.41 -\$6.49 3B \$5.53 -\$8.86 \$4.30 -\$6.57 DH \$8.00 \$0.00

What kind of effect, if any, will the information in this article as well as the dollar days article have on your draft strategy in 2012?

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bubba3m
1/18
I have observed that, for our league, the \$20 OF'ers tend to provide good returns- players like Bruce, Kemp, and Justin Upton all went in that range last year, for example. Also, the last two winners of our league went against "stars and scrubs" and filled their rosters with \$9 - \$20 players, possibly using a Moneyball-ish strategy of going zig when the market is zagging, or possibly just due to luck. I was thinking about using that strategy, and your analysis has sealed it. I think you're pointing out a market inefficiency. A natural follow-up question to your article would be, "but what causes that inefficiency?". Also, I'm curious to know how you selected positions for players with multiple eligibilities.
BurrRutledge
1/19
This is great stuff, Jason.

I think it reinforces what I've observed in snake drafts, too. Selections in the very top rounds are important, but rounds 4-9 and 20+ can really make the biggest difference over the course of a season.