This is a follow up to our post on how crowdsourcing scores can work. In that post, we talked about how who-picked-whom is a crude metric of crowdsourcing predictions, but it’s main gap is that it doesn’t provide margin of victory to determine a more accurate crowdsourced prediction. As a result, even if the majority thinks that the home team will cover the spread, it does not indicate what the crowd believes the margin of victory will be and whether there is value in the spread set by the sportsbook.
Paul and I were talking this week about how to present spread prediction alongside margin of victory. Here’s the basic idea:
|Team||CSS Score||CSS Margin of Victory||# Win Predictions|
Total Predictions: 300
CSS Game Total: 51
CSS Spread: Home -3
I’ll quickly summarize so everyone understands what this means. “CSS Score” is the average of all of the score predictions for each team (Σ(Away)/#Predictions),(Σ(Home)/#Predictions). The “CSS Game Total” is the average of all of the totals for each prediction ((Σ(Away) +Σ(Home))/#Predictions), and the CSS Spread is the average delta of all the home scores and away scores (Σ(Home) – Σ(Away)/#Predictions).
The Margin of Victory (MoV) is the data point I want to address. Where does MoV come in? This is kind of fascinating to me, actually. Margin of victory would be, for example, the delta between the home team’s score and the away team’s score only in games in which the home team’s score is greater. This would indicate a bit more than the spread by itself. As I discussed before, if more people believe that the home team will cover the spread, it doesn’t necessarily tell you how confident they are. Margin of victory provides that detail. So, let’s say that the CSS spread predictions is Home -3. That tells you where the average delta is across all predictions. But MoV provides a window into how confident users are in a win by either team. More people think that the away team will win, but the people who think that the home team will win think that they’ll win by a larger margin. In that scenario, you’d see the CSS Spread lean more towards the home team.
I am very excited to see where this data takes us. There is not enough of it to go around, and I think it’ll provide a lot of insight and advantage when making your picks.