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Sunday 30th Apr 2017

A few weeks back, I described a means to graph a draft by estimating the expected return per draft spot by averaging year end values for hitters and pitchers, grouping them together and blocking them off by rounds. Today I will present the actual data from 15-team mixed leagues and we’ll discuss some interesting repercussions. Here’s the data with the pick across the top and the round down the left side:

PICK 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 DIFF AVE
Round 1 49 45 42 40 38 37 36 35 34 33 32 31 30 30 29 20 36
Round 2 29 29 28 28 28 28 27 27 27 27 26 26 26 25 25 4 27
Round 3 25 24 24 24 24 23 23 23 23 23 22 22 22 22 22 3 23
Round 4 22 22 22 21 21 21 21 20 20 20 20 20 20 20 20 2 21
Round 5 20 20 19 19 19 19 19 19 19 19 19 18 18 18 18 2 19
Round 6 18 18 18 18 18 18 18 17 17 17 17 17 17 17 17 1 17
Round 7 17 17 17 17 16 16 16 16 16 16 16 16 15 15 15 2 16
Round 8 15 15 15 15 15 15 15 14 14 14 14 14 14 13 13 2 14
Round 9 13 13 13 13 13 13 13 13 12 12 12 12 12 12 12 1 13
Round 10 12 11 11 11 11 11 11 11 11 11 11 11 11 11 11 1 11
Round 11 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 0 10
Round 12 10 9 9 9 9 9 9 9 9 9 9 9 9 9 9 1 9
Round 13 9 9 9 8 8 8 8 8 8 8 8 8 8 8 8 1 8
Round 14 8 8 8 8 8 7 7 7 7 7 7 7 7 7 7 1 7
Round 15 7 7 7 7 7 7 7 6 6 6 6 6 6 6 6 1 6
Round 16 6 6 6 6 6 6 6 5 5 5 5 5 5 5 5 1 5
Round 17 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 1 5
Round 18 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 0 4
Round 19 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 1 4
Round 20 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 1 3
Round 21 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0 2
Round 22 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1
Round 23 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1

Also included is the difference between the first and last pick from each round and the average per round.

Note how steep the drop is in round 1 and how by round 4, everyone within the round is basically the same player according to APE. It’s also interesting to note how the descent of the average per round is also not linear; the slope is steeper at the top of the draft.

The first application of the above data is to realize that the number in each cell is the expected return for each pick. Look at how little return is needed at the end of the draft. Harkening back a few weeks, this is why I am completely confident at some point, you’ll find someone at every position that will at minimum break even with respect to their draft spot.

To help put things into perspective, if you take the typical year end standings, the champion of a 15-team league has the equivalent of $360-$380 worth of stats which is at least $100 more than the theoretical break even amount. In other words, the winner distributes at least $100 over and above the break-even return of investment numbers in the chart above.

This begs a very interesting question. Where is the best place to aim to get the maximum return on investment?

Think about this for a second. As incredible as Mike Trout’s season was last year, he only earned $45. Granted, it was almost all profit as most of his owner took him late, but if he were to repeat that season this year and earn the same $45, he would give you a negative return on investment if you take him #1 overall. While there are different degrees of expected regression, everyone agrees Trout’s numbers will fall (though some claim the added games will compensate). The point is, the risk far outweighs the reward when it comes to drafting Mike Trout #1 overall.

Due to the steepness of the slope in the first round, my personal objective is to shoot for the average, which is $36. In other words, I will choose the player I feel has the best chance of earning as close to $36 as possible. I am more concerned with less downside than I am with greater upside. I already need to find another $100 worth of stats in order to win. If I mess up the first round pick, that puts me even further in the hole. For those reason, I chose Prince Fielder as the sixth overall pick in mixed LABR. I’d rather lock in at least $30 and take a small $6 loss than risk an even bigger loss.

Personally, I feel the most efficient area to pick up the necessary $100 of profit is in the latter half of the draft, which is another reason why I don’t want to leave potential on the board by drafting for scarcity. For every dollar’s worth of stats you leave on the board, you need to add that onto the $100 you already need to find. If you take a chance in the 14th round and it fails, you’re out $7, which is a lot easier to make up than if you take a shot in the 2nd or 3rd round and put yourself $25 in the hole.

The title of this column is Chance Favors the Prepared Mind. One way to prepare for chance is to have a lower tier player at as many positions as possible so if someone were to emerge in season, you realize the maximum return of investment since they will be replacing a late round player not expected to earn more than a handful of bucks. If you put draft your middle infielder in the 12th round and you have Steve Lombardozzi on reserve and he becomes the second baseman for Washington and earns $15, you pick up a rather modest $6 of profit if you replace the 12th rounder. But if you put him in place of a guy you drafted in the 21st round, you realize a more useful $13 of profit. Having a few multiple eligibility players helps in this effort as well.

In short, the goal of your draft should be to set yourself up to pick up the necessary $100 worth of profit. Some of that is put on your roster via superior player analysis and game theory. Some of it is clever managing in season via free agent pickups and reserve management. Using the chart above can help frame your game plan to use your assets in the most efficient manner.

Next week, we’ll talk about the game theory aspect of the puzzle and the means I use to put more stats on my team (at least I hope) than my competitors.

Comments   

0 #1 william conklin 2013-03-11 23:06
Might this be useful if evaluating a potential trade of draft picks? Like the NFL Draft charts that you read about.

Trades of draft picks are not that common, but if presented with the opportunity, I'd love to take advantage.
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