Friday MLB- 5 Plays to win 3U, 2 Qualifying TR Picks

Can you explain the whole Teamrankings thing?

Decision Tree Model

Our Decision Tree Model is the output of a machine learning algorithm that views every college basketball game since 1999 through the lens of hundreds of input variables, ranging from contextual information like the distance traveled by squad to team statistics like effective field goal percentage.

The algorithm does what might be convenient to think of as complex, high volume, statistically significant trend analysis. It repeatedly partitions the games into smaller and smaller subsets based on the values of one or more variables. Each split is chosen so that the win probabilities of the teams in each group get further away from 50% and closer to 0% or 100%.

But it also takes care not to be too overzealous in the splitting, checking to be sure that the splits are meaningful, and not just the products of a small sample size. In the end, it’s left with a set of rules along the lines of: If Variable1 is greater than 100 and Variable2 is less than 7 and Variable3 equals “YES” (and so on) then the win probability of TeamA is 64%.

Complicated enough for you? OK, now here’s the twist: we actually have about 100 different decision tree models, each of which look at a different subset of variables. The results of all the individual models are averaged to get the overall win probability. This helps reduce the effect of crazy outlier results, and ensures that we have multiple reasons for picking a certain team to win.

Outputs: win odds, point spread cover odds, over/under odds

Strengths: This is our most complex model, incorporating the largest amount of information, so if there’s an obscure nugget of knowledge hidden deep within our database, this is the most likely place for it to show up. It also generally has proven so far to be our most accurate model, although prediction performance varies by sport.

Weaknesses: Since it is partly based on historical trends, changes in the way the game is played or in other associated data can lead the model down a new, unexplored path, where that trend no longer applies. The complexity of the model also makes it next to impossible to explain to an average fan. We know what the output is, but we never know exactly why the model gave us a specific number as an answer. We’re just shoveling data into a computer and trusting it, which is what most advanced quantitative prediction systems do, but it still makes some people nervous.

I'm sorry you had a rough day yesterday.  It happens to everyone.  This business get humbling quick.  Better luck today for sure.