Until now, Trackkarma excelled at one thing: presenting clearly everything a statsbook already contained. Valuable data, but the analysis stopped where the statsbook stopped. Our new statistics go much further. Beyond the raw data, Trackkarma now deduces and computes a wealth of brand new stats and insights that no statsbook holds on its own, from packs and jammer-pack chemistry to full opponent scouting. From now on, you can surface the trends hiding behind the numbers. Let's take a closer look!

Until now, a game's statistics came as long, fairly dense tables. Everything was there, but reading through the data took real effort.

The game, team and member statistics pages have all been completely reworked around crisp charts and explanatory cards. Key figures stand out more, and trends can be read at a glance!

Let's be honest: a statsbook is not the easiest thing to read. Behind every abbreviation and every differential hides a definition that not everyone necessarily has in mind. So we did not stop at displaying the data more clearly: we added explanations almost everywhere, on individual statistics, on every chart, and on the most complex metrics. That way, whether you have been playing derby for years or only just joined your league, you can always tell exactly what you are looking at, and why it matters.

A pack is the four skaters, a pivot and three blockers, lined up together for a jam. Until now, statistics only ever graded them one by one, never as the unit they actually skate as.

Trackkarma now aggregates statistics for every pack fielded during a game, and across a whole season for a team. You can see which four-skater combinations spent the most jams together and, more importantly, which ones posted the best average point differential. It is the clearest way yet to spot the blocker lines that can make the difference.

Where the previous section looks at packs as a unit, this one takes the jammer's point of view. Jammers are never truly alone out there: the points they score, and the points they concede, depend heavily on their own blockers and the pack they are facing.

For each jammer, this section aggregates the packs they were paired with. For a pack from their own team, the value shown is the point differential while they skated together. For an opposing pack, it is the differential while the jammer was facing it. You can finally see which blocker combinations help your jammers score, and which opposing walls give them trouble.

A pair is simply two players on track at the same time, regardless of their positions. Some skaters just click better than others, and until now there was no easy way to measure it.

Trackkarma now surfaces your most frequent pairs and, more interestingly, the pairs with the best average point differential. It is available at every level: within a single game, across a whole season for a team, and on each member's page. A great tool to understand who brings the best out of whom.

The rebuilt pages also go deeper into the flow of a game. You can now open the detailed lineups jam by jam, see exactly who was on track, who had lead, and who spent time in the box.

Penalties are broken down by type, so you can spot recurring patterns. And every game now tracks its total number of lead changes, as well as each player's share of leads.

This is the feature we are most excited about. Trackkarma now maintains a knowledge base built from public WFTDA statsbooks, covering a large number of teams and games from around the world.

When you prepare for a game, Trackkarma tries to automatically match your opponent to a team in the knowledge base. When it finds one, you get an instant analysis: their record, their scoring, and a breakdown of their key players by role, jammers, pivots and blockers. Enough to size up their strengths well before you face them.

And if no automatic match is found, you can search the knowledge base yourself, by team or league name, and choose exactly which games are used in the analysis.

Scouting an opponent as a whole is already valuable. But sometimes you will want to go one step further, and see how your own jammers would fare against their packs.

That is exactly what the advanced analysis does. You pick the knowledge base games to study in detail, and Trackkarma re-analyzes their statsbooks on demand to rebuild that team's packs and pit them against your jammers. Trackkarma even pre-selects the games that look like they were played against your team. You can then see how your jammers performed against the opposing walls they have faced in the past.

Every one of these additions, the redesigned pages, packs, jammer-pack chemistry, pairs, detailed lineups, opponent scouting and advanced analysis, is available not only on the web app but also in the Android and iOS mobile applications.

This is by far our largest statistics update to date, and, as always, it was shaped by your feedback. Keep it coming, and enjoy the rest of your summer!