As soon as the baseball season comes to its inevitable and saddening end, baseball, as it does each year, will enter the offseason. For the fantasy baseball community, this means we will be entering ranking and projection season. After following “our players” and players of interest all season, we are now asked to take an all-encompassing look at the league’s baseball players. The result of doing projections periodically, as opposed to continuously, is that we are likely to invite certain biases into our processes, which can negatively impact our results. We will take a look at why we do periodic projections, the biases that come with such a process, how these biases manifest themselves, and some ways to hopefully de-bias our process.
The devil’s advocate in me asks, “if periodic projections causes certain problems, why not do continuous projections?” The short answer is that doing continuous projections is not feasible or desirable for most of us. A computer program could certainly perform continuous projections, but we—as mere people (note: people are awesome)—do not have the ability to continuously adjust our valuations on such a large scale. Sure, each time we watch, read about, or hear about a player, our impression of said player will be altered or reinforced consciously or subconsciously, but that is not what I am getting at. Rather, what I mean is that we cannot watch all players play every one of their plays, and we cannot fully analyze all of what we see or all of the available data. The result of all this humanness is that we can really only fully update our projections on a league-wide basis come decision times; those being the offseason for auctions and drafts, as well as, to some extent, the trade deadline. While we constantly update our valuations for the players we follow, my assumption is that very few people follow every player and those who do probably do not do so diligently enough to properly continuously update each player’s projection.
Alas, all is not lost. There is nothing theoretically wrong with doing evaluations periodically; however, as mentioned previously, there are certain biases that tend to affect periodic valuations. In order to improve our evaluations and projections, we need to remove these periodic projections-frequenting biases. In order to do that, we must know these biases and their impact.
The Recency Effect and its Coconspirators
(Before everyone gets too distracted; I agree, “coconspirator” is a really weird looking word.)
The recency effect, as you have probably already guessed or knew, causes us to overweight recent events. While the recency effect is the most obvious cognitive bias present for our offseason projections, it is certainly not the only game in town. Attentional bias, the clustering illusion, confirmation bias, and choice-supportive bias also have an impact. These other biases, whether they work to cause the recency effect or strengthen it, habituate our year-to-year valuation ecosystem. Before I proceed, some definitions (from changingminds.org and wikipedia.org) follow:
- Recency effect: given a list of items to remember, we will tend to remember the last few things more than those things in the middle. We also tend to assume that items at the end of the list are of greater importance or significance.
- Attentional bias: the tendency of our perception to be affected by our recurring thoughts.
- Availability heuristic: the tendency to overestimate the likelihood of events with greater "availability" in memory, which can be influenced by how recent the memories are or how unusual or emotionally charged they may be.
- Confirmation bias: our old friend; the tendency of people to favor information that confirms their beliefs or hypotheses.
- Choice-supportive bias: the tendency to retroactively ascribe positive attributes to an option one has selected.
Instead of an exhaustive theoretical discussion on each of the above, let us take a look out how different aspects of baseball become over weighted because of these phenomena:
- Playoff Performances: pretty much periodic projection bias Yahtzee. At the time of our offseason projections, playoff performances are the most recent (outside of spring training, possibly). They are the most scrutinized and discussed; thus, making them the more likely to be available in our memories. And finally, with no other games going on, we repeatedly observe the same players (recurrence). Combine these with the opportunity to ignore our incorrect regular season projections (examples: Justin Verlander in 2013, Eric Hosmer in 2014), and we can therefore see how playoff performances can do a number on our projections.
- Offseason Moves: because only a fraction of players change teams or get new contracts in any given offseason, the ones who do so will get a disproportional amount of attention. The more attention, the more articles written, the more likely these players are to be disproportionately on our mind come projection or decision time.
- September Call-ups: another deadly behavioral combination: recency and availability to go along with small sample size and hope.
- Spring training: similar to September call ups, but with more choice-supportive bias involved.
- Second-half performances: again, an opportunity to see what we want to see in the mountains of data available to us.
Conclusion and Recommendations
So we get it, because of the nature of our game and our minds, we are more likely to come to the conclusions that we want to come to. For starters, try to take advantage of this when your co-owners are easily swayed by these phenomena. It only takes one owner to believe in a Mike Moustakas spring training breakout in order to cash in. The more difficult part, is avoiding being the owner that others are cashing in on. The question is therefore, “How do we fight these biases?”
I will start with the usual advice because I think it is the best advice: Check your assumptions and search for disconfirming information. To specifically counter the phenomena listed above, we should start with the biases, not the players. I know, I know, digging into individual players is the fun part, but doing a little legwork up front makes it less likely for us to overlook these biases while in the middle of a player analysis. So what does this look like? First, start by listing and keeping lists of players that fit each of the above scenarios (playoff performances, off-season moves, September call-ups, etc.). Additionally, keep a similar list for players you were wrong on, as we can be both likely to want to overlook our mistakes and to be overreactive to our errors. After that, do the same for players who did not make these lists, but who fit each of the above biases. Finally, before doing player analyses, make sure to check your lists. By knowing how we might be biased, we are more likely to find disconfirming information and less likely to blindly fall to these biases. Will we ever be perfect? No, but perfection is boring anyway. We can, however, certainly be better and knowing how we will most likely err is a step towards improvement.
Lastly, thanks to the very excellent Jordon Gorosh for the idea for the article.
Thank you for reading
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