Statistical data miner: A tiny data-mining program to learn about your favourite sports

By now, the statistics profession is probably familiar with StatCast.

It’s a little-used, mostly obscure tool that shows you stats about your team, and provides you with the statistics you need to understand them.

But StatCast doesn’t necessarily have to be a StatCast, as some statistics-related services, such as the BBC’s Sports Personality Index, also have stats-mining tools built in.

We’ve got some other interesting stats-related apps to look at in this article.

There are plenty of tools out there, but StatCast is the one I’d recommend the most.

This article is part of our Stats series, in which we’re looking at data mining in a wide variety of ways to understand how the world works.

In this article, we’ll explore a new statistics-mining tool that we’ve discovered.

If you’ve got any tips or suggestions for other stats-based services, please leave them in the comments below.


Stats-mapped a team’s stats The Stats-Mapped project started by two statistics-professionals in the UK.

The first, Matt Bostock, was a statistician in the United Kingdom, and has also been an author of a number of books on statistical analysis.

His work is focused on sports, and he’s done a lot of research on statistics in sports.

He’s been doing it for 20 years, and his project began as a way to understand the role of the game in the sporting landscape.

He wanted to see what the teams’ stats were, and then see if he could find trends that could be extrapolated to a broader, more comprehensive picture.

For this project, Bostamp mapped a team of soccer clubs over a period of time.

Bostocks team used a simple statistical model: he looked at the number of goals scored per game for each team, as well as their points per game and their goal difference per game.

In addition to analyzing these data, he was able to find out what the average amount of time between shots was between each team.

This is an interesting statistic, as it’s a very useful indicator of the amount of possession a team has in the first place.

It tells us how often the team plays and is forced to play.

But Bosters team also looked at other metrics like the quality of the opponents’ chances created and conceded per game, and how many times the team was in possession per game versus the opposition.

The team then looked at how these variables affected the teams success over a given period of games.

For instance, a team in the bottom half of the table will be more likely to score in the second half of a game, but it will lose possession in the process.

Boes team also used this information to predict the outcomes of games, which is how you can learn what your team is doing and how to improve.

StatsMapped then did some statistical research, using a mathematical model called the “Bayesian inference” technique.

This technique is a great way to find patterns in data that aren’t there, and to see if they can be extrapolated to other data sets.

For example, if a team played a lot better against the top teams, and the other teams played less well against them, this could help predict how the teams would perform in a more competitive league.

To understand how this process works, let’s look at a typical soccer game.

A team has three options in the attacking half of its field: two forwards, one left, and one right.

The left side is the attacking zone.

There’s a long line of players between the two attacking zones, and these players will all run towards the ball, with the ball in the centre of the pitch.

The ball is in the forward position, and teams can’t score from this position.

There is a wide space in the middle of the field, with three defenders at each end of it.

This means that if the team is able to get into the forward line, they have the ability to create chances from there.

To get an idea of how the stats-mapper would work, let us break down a typical game.

Let’s say that a team plays a long ball up the right side of the goal, and there’s an opposing player close to the ball.

This player will run up the left side of his own line and try to take the ball off the opposition’s goalkeeper.

The opposition will react, and will counter this move by making a similar move.

Now, what happens if the opposition tries to get in that same line and gets a free kick?

The goalkeeper will be in the right line, but he’s not in the left line.

In other words, if the opposing player takes a free-kick from the left half of their line, the goal is a goal for the opposition, and if the goalkeeper takes one, it’s for