Updated: Sep 13, 2020
Recency bias is a cognitive bias that favors recent events over more historic ones. This bias can often lead to players temporarily rising in price as traders value player's recent events (Strong performances/transfer links/media pull etc) greater than events from the past.
The influence that this has on the market is very clear. For example, players often increase in price when they have a one-off strong performance, even when such an event does not change a player's underlying value. Analysis throughout this blog will explore the impact of recency bias on the prices of players after they have hit 250+ PB scores.
The spreadsheet below includes all players who have hit PB scores of 250+ this season up until 14/07/20. The spreadsheet includes the player's:
Start of season price
Season average price
Price one day before they hit the relevant PB score of 250+
Price one week after they hit the relevant PB score of 250+
Price one month after they hit the relevant PB score of 250+
We can gain further insight into the huge impact that recency bias has on player's price by analysing the price movements within the spreadsheet below.
*This spreadsheet was put together using data from the Index Edge Spreadsheet
PB Scores Over 250 Price Movements Excel Spreadsheet
PB Scores Over 250 Price Movements Google Spreadsheet
Price movements after first PB score of 250+
The first tab in the spreadsheet includes every player's first PB score of 250+ during the 2019-2020 season.
Over the last 12 months:
The average price increase in these players was 46.78%
The average price increase per week was 0.9%
The average price increase per month was 3.89%
Following a player hitting a PB score of 250+, player's prices increased considerably:
The average price increase after one week was 20.98%
The average price increase after one month was 29.18%
Hitting a high PB score can prove to trader's a player's potential to earn match day dividends in the future.
However, there is no doubt that recency bias has a huge impact on the demand for players after they hit a PB score of 250+. Furthermore, often once a player has already hit a 250+ it is too late to buy into the player for matchday dividends and they may fail to hit such a high PB score again in the future.
The average price of these players one month after they hit their first PB score of 250+ was £1.44. The average price of these players now is £1.41. This shows how many player's prices have dropped since hitting peaks caused by recency bias.
Form and improvements can of course lead to players having a stronger chance of earning dividends in the future, but identifying if a player has just temporarily hit form or will be able to replicate such strong performances in the future is key.
Those who went on to hit 2 PB scores of 250+ throughout the season tended to increase much more and most currently have higher prices than they did one month after their first PB score of 250+.
Belief in the law of small numbers
The law of small numbers refers to the incorrect belief that small sample sizes will be representative of larger data patterns. Alongside, FOMO, herd mentality and recency bias the law of small numbers may explain why some high PB scoring players were able to hit such high prices earlier in the season following the dividend increase.
Due to hitting high PB scores at the start of the season, a number of players became massively overpriced. It is therefore important to be aware of sample sizes when considering PB scores.
Stefano Sensi, Nicolas De Preville, Karim Benzema, Jesus Navas, Marcel Halstenberg, Alejandro Gomez and Miralem Pjanic have all dropped over 50% in price since their price one month after their first 250+ PB score.
Regression toward the mean
The regression toward the mean states that if one result is extreme compared to the average then the next result will be closer to the average. Therefore, it can be argued that once a player has hit a 250+, they are less likely to do so again in their next game.
In terms of prices, because the average price often rises following a high PB score, the regression toward the mean suggests that the price will also drop in the future and get closer to the player's average price. However, on Football Index this largely depends on general growth and a number of other factors.
Overall, the regression toward the mean suggests again how it can be unwise to buy into a player once they have already increased in price after hitting a PB score of 250+.
Recency bias has a huge impact on price increases and for a number of reasons it may be best to avoid players who have already increased in price recently due to hitting a high PB score.
Although this blog has focused on PB scores in particular, recency bias leads to player's increasing in price for a number of reasons. For example, it can explain how Ighalo hit £2.15 in March when he was earning media dividends regularly at the time.
Of course, some players will remain strong value after rising on the back of a high PB score as we have seen with plenty of players this season including Trent Alexander-Arnold and Jadon Sancho.
Furthermore, recency bias can create opportunities to pick up comparatively valuable players who are out of form and have subsequently dropped in price. Though, again, a range of factors will need to be considered, most importantly how likely the player's form is to return and their age.
Overall, identifying key players with real intrinsic value and attempting to make objective decisions on players by valuing their historic data equally to more recent data is key to avoiding recency bias and making better trading decisions.