Outcome Bias

We have all had this experience. You set your DFS lineup with what you thought was good rational. You took some clearly undervalued players, some studs you liked, and you submitted your lineup. But when the day is over half your players laid an egg, and you fail to cash. You look back at your lineup and you think “How could I have picked this lineup? It was so obvious they were going to fail.”

The truth is most of the time it wasn’t obvious, and it is very important in improving your DFS strategy that you don’t change your strategy on the whims of your failings and successes, especially when there is so much luck involved. Outcome bias is the cognitive bias which states that we have the tendency to judge a decision by its eventual outcome instead of based on the quality of the decision at the time it was made. We are hard wired to question our decision making when the results tell us our lineup choices were shit. When all our entries pan out, we think we were brilliant.

However, results can help us learn from our mistakes and give us new ideas on how we can improve our decision quality at the time we are making our lineups.

On 11/13/13, Tristan Thompson seemed like a good value against the Minnesota Timberwolves. Thompson had been averaging a double double so far that year, and the Timberwolves were leading the league in field goal attempts per game, creating many opportunities for Thompson to grab rebounds and block shots. Both teams played at a fast pace creating many scoring opportunities for Thompson as well.

Despite a lot of positive factors in Thompson’s favor, Thompson had a paltry line of 10 points, 6 rebounds, and 2 blocks. For his price tag, that simply wasn’t good enough. What did we miss? It turns out probably nothing. Minnesota was up over 30 points on Cleveland by the end of the 3rd little over 20 minutes of total action as both teams rested their starters for the 4th quarter. And there was really no way to know that was going to happen. The Timberwolves were only favored by a small margin.

Thompson bit us in the ass that night, but it’s important we don’t stray away from him completely, since that night was clearly poor variance.

On that same night, Steven Adams of OKC seemed like a good start in fantasy basketball. Injuries on the Thunder meant that Adams was going to start that night and he would log much more minutes than previously. His minimum price tag seemed inappropriate.

However, Adams only managed 12 fantasy points that night. What could we have been missing? Maybe not anything, maybe Adams simply did not have a good game and we couldn’t have predicted it. But there were other factors we could have considered. Adams was a rookie, a low draft pick, who had been minimally successful in his games this year. He was also on a team of super stars, Kevin Durant and Russell Westbrook, who were likely to pick up the offensive slack in Ibaka, the injured players, absence. It seemed unlikely then that in place of Ibaka he would be someone that would step up, be aggressive, and improve his fantasy output. Durant in fact did pick up Ibaka’s slack, posting a fantasy night of nearly 60 points.

If DFS players would’ve considered those factors, he probably wouldn’t have gotten as much play that night.

When we have a player underperform in relation to our expectations, don’t assume you have made a bad decision. There is a lot of variance in DFS and players will often underperform for reasons out of your control. It’s important when a player underperforms that we do not overreact and miss out on future opportunities with that player. At the same time, bad performances can be a learning experience, sometimes you do miss certain factors and bad performances can help guide you to what those factors may be. To be a big winner at DFS it’s very important you are able to tell the difference between the two.

View all posts by Daniel Steinberg
Daniel Steinberg

About the Author

Daniel Steinberg Daniel Steinberg is a former bond trader at a multi-billion dollar proprietary trading firm in Chicago. He uses his knowledge of statistics and his creativity from his career as a poker professional to create the most advanced Daily Fantasy statistical analysis that you will find anywhere. Follow him on twitter @DanielSingerS

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