I have used football results in many of my previous pattern match blog posts, as this is a good example of data with a large number of transactions (matches) for each team. The data structure is Team->Match (i.e., a team has lots of matches). We can use the pattern match technique above to find teams that keep on repeating the same result type (winning or losing!), or who cycle through the different results (the very definition of supporting your team through thick and thin!)?
We can do this using the same cp number philippines technique as the holidays example above, but since a team can only have 3 possible results (Win, Loss, Draw) before repeating one of them, we only need to use ?3?2?1 as the longest sequence of different results. A team that is always repeating the same results will have an average difference length of 1, and a team cycling through all possible results in the same order will have an average difference length of 3.
Let us consider English league football on a season-by-season basis (3). In general, there are sequences of 38 to 46 matches depending on the season and the division a team was playing in. The pattern match aggregation in the expression is then used in the final statistic as a Mean to give the relevant figure for each season.
Our first view below is at the results for the last 3 completed league seasons, sorted so we can see the lowest scoring teams in the last season. Manchester City (lots of wins) score lowly as they have had less opportunities to switch result types. However, their score in 2020 of 1.58 was significantly higher than the 1.26 and 1.32 scored by them and Liverpool in 2018 in the title race that went down to the deciding day. Other teams such as Norwich and Sheffield United scored lowly by this metric as a result of losing a lot of games.