• Existing tools that value draft positions are a good start
  
• While a good start, they tend to assume perfect knowledge of the future
  
• We incorporated the uncertainty of the future and saw that it can impact the value of picks

This week we will use the same model to investigate the value of a mistake in the draft. Throughout the article in each simulation all picks except one (i.e. 47 out of 48) will choose optimally. Then, for a single pick in the draft we will layer in increasing likelihood of underperformance.

We make a slight change this week to the language we use for the drafting assumptions. What we called “Realistic” last week we will now refer to “Moderately Pessimistic”. And what we called “Pessimistic” last week we will call “Very Pessimistic” this week. This is meant to clarify that we do not view the moderately pessimistic assumptions as being calibrated to reality – but rather just being somewhere between very pessimistic and optimistic.

We define our tests with the goal of leading us to a useful conclusion that you might be able to use in your upcoming fantasy football draft.

We use the same baseline assumptions as last week for the most part:

• 12-team PPR league, with 15 rounds
  
• Focus on rounds 1, 2, 3, and 4
  
• Assume the following distribution of picks:

In other words we assume the first 5 picks are always RB and no TE is picked before round 3. We assume round 2 will on average have 4 RB’s, 2 QB’s, and 6 WR’s picked but that will vary by scenario of the simulation, etc.

Our conclusions are not unique to 12-team PPR leagues, and would not change a whole lot for many changes to these baseline assumptions. We stick with these as a starting point for simplicity.

We define the value of a player based on their total points scored in excess of the last drafted player for that position, assuming 48 RB’s, 24 QB’s, 60 WR’s, and 12 TE’s will be drafted. In other words, we focus on their value in excess of a waiver wire pick. Then we run 1000 simulations of drafts, letting the choice of each position vary for each draft – and we look at the average value of each pick over all the simulations.

Baseline Results

Using our usual methodology, we normalize the value of the #1 pick under the baseline assumptions to equal 100. Then we look at the sum of the value of the top 4 draft picks under the baseline assumptions, where all teams draft “optimally”. Recall that under this set of assumptions Tom Brady would go undrafted in 2008. The chart below shows the baseline results for the average of the sum of the value of the first four rounds of draft picks by team:

Two points stand out here. First the value of the first four rounds of picks for teams 4-12 is pretty similar under these baseline assumptions. Second the value of the top 3 picks has some separation from the value of the picks of managers 4-12. This is a direct result from our assumption that the top 3 picks of the first round are more reliable than the rest of the first round. This happens to be consistent with the view of a number of experts who view MJD, AP, and Forte as being a step above the next set of RB’s. To the extent your personal view differs from this, this should be kept in mind.

Approach

We start by focusing on the 6th team in the 12-team draft. We let everyone else in the draft continue to always draft “Optimally”. We then model the concept of a Team 6 “mistake” in the fantasy draft by letting team 6’s draft result drift from “Optimal” down to “Optimistic” down to “Moderately Pessimistic” down to “Very Pessimistic” all the way down to “100% Bust”. In reality managerial draft success will always be between optimal and 100% bust – and usually be between our Optimistic and Very Pessimistic cases. In this framework we will try to get some perspective on the cost of a mistake in the fantasy draft.

Case Study of Team 6 – Let Assumptions Drop from Optimal to Optimistic

We let exactly one pick of Team 6’s drop from “Optimal” results to just “Optimistic”. We re-run our simulations under these assumptions, assuming that suboptimal pick is the round 1, 2, 3, and 4 pick separately.

The expected value of team 6’s top 4 draft picks drops by about 10% if the first round pick drops from being optimal to only optimistic. The drop grades up to 5% in the later rounds. Note the drop is most severe if we introduce a non-optimal draft decision in round 1 and least severe if we introduce it in round 4.

Case Study of Team 6 – Expand for Higher Likelihood of Failure

Not surprisingly, if we move from optimal to moderately pessimistic draft expectations the drops are more severe. The drop is most severe if we experience it in round 1, and least severe in round 4.

Obviously, if we move from optimal to very pessimistic draft expectations the drops are even more severe. The drop is still most severe if we experience it in round 1, and least severe in round 4.

If we move from optimal to 100% bust draft expectations (i.e. the most extreme theoretical case) the drops are obviously even more severe. The drop is still most severe if we experience it in round 1 and least severe in round 4.

OK, that’s nice. But What Can We Actually Do With This?

It’s obvious if one of your picks is more likely to do badly that it will reduce the expected value of your picks (all else held equal). The analysis above helps put some numbers to this, under one reasonable set of baseline assumptions, to give you some perspective on just how much it “costs” to make a pick that is more likely to be a mistake.

So what can we learn from this?

1. If you are going to go with a risky pick, it is riskier to do it in round 1 than the later rounds.
   
   This lesson shines through with every chart above. Namely – the impact of a drop in the likelihood of draft success is always more severe in the earliest rounds than the later rounds. The potential for extreme upside above draft expectations is also higher in the later rounds (simply given the fact that the expectations are lower). These points support a draft strategy of minimizing risk in the first round.
   
   Here’s an example: Let’s say with your round 1 pick you are thinking of taking someone the experts view as approaching the end of his career with high injury risk (e.g. Brian Westbrook or Steven Jackson). If you are weighing one of them against a younger option that experts view as being safer, but ranked slightly lower – you might want to consider taking the “worse” (i.e. lower expectation in the eyes of the experts) but safer option. For small expected differences, it might make sense to just pass on that overworked injury-prone back and use your top pick to choose an option that seems less likely to bust at this point.
   
   If you are moving in the direction of taking a “risky” pick in round 1, you should seriously consider what the team is going to look like if your pick gets injured at the start of the season and doesn’t play again. What has happened in the past when your pick has missed a game – has the offense stepped up and filled the hole with existing resources? Or has that unstoppable Oakland run defense subsequently shut them down?
   
   I would feel more comfortable taking a risky RB if I had confidence in the team’s run game with the backup (e.g. San Diego if L.T. goes down) than I would if I lacked confidence in the offense (e.g. St. Louis if Steven Jackson goes down).
   
   
2. Taking on unnecessary risk in round 1 picks can severely decrease the value of your picks.
   
   This is in some ways a continuation of the above point. As the likelihood of a single pick to bust or underperform increases, the value of your first 4 round picks can decrease drastically. In any given season you might get lucky and not observe this in your results, but in the long run there is a real cost to taking on unnecessary risk. The analysis above attempts to quantify this cost under one set of relevant assumptions.
   
   Round 1 is not the place to take on a lot of risk. It’s just very difficult for players to generate enough upside above their expected performance to compensate you for this higher risk. As your draft proceeds, picks that have a higher than normal potential to bust or severely underperform can be worth the risk, because they have a chance to overperform (e.g. Plaxico Burress this year might be one example of a high-risk player that has potential to overperform, depending on how the next month pans out). But common sense combined with the results above all point in the direction of minimizing risk (as much as possible) in your round 1 pick. Save that gamble for later.
   
   
3. Get your fantasy advice from a range of sources.
   
   FFToday.com is a great resource – use it as one tool in your basket. Weigh the opinions of a lot of resources, and make your own adjustments based on your own views. Getting more information will give you more potential to see warning signs of a potential bust or severely underperforming pick. That in turn increases the expected value of the picks you have in your hand. The analysis above provides additional perspective on how valuable that can be.