Gaining a Competitive Edge with Advanced Analytics

Scouting reports containing interactive visualizations and advanced stats designed to help coaches, players, and analysts outthink and outperform the competition.

Gaining a Competitive Edge with Advanced Analytics

Interactive visualizations and advanced stats designed to help coaches, players, and analysts outthink and outperform the competition.

Overview

After months of development, the first version of our data-driven scouting report is ready. Created by players with input from coaches, the report focuses strongly on on-field, actionable insights. We leverage models from shownspace.com to surface numbers, patterns, and tendencies that can influence real decisions, whether it’s shading inside against a particular cutter, targeting a key offensive connection, or anticipating pull placement.

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Team Level Metrics

Predicted Rosters

Our roster model analyzes past game appearances to forecast the 20 players most likely to suit up in an upcoming match. Roles are inferred from historical usage, whether as a handler or cutter, and whether they predominantly appear on offense or defense. From this, we extract the top seven probable O-line and D-line players, giving coaches an early look at likely matchups. Within seconds, you can form a baseline picture of the personnel you’ll face, before official rosters are even released.

Interactions

At the team scale, one of the most revealing tools we offer is the chord diagram. This visualization complements the individual connection data you’ll see later in the player cards, but flips the perspective: instead of focusing on one player’s network, it maps the adjusted Expected Contribution (aEC) for every significant passing connection, alongside a second diagram with completions per 100 possessions. The effect is a panoramic view of team interactions paired with a precision lens for spotting where value is truly generated. By normalizing completions per 100 possessions, this visualization levels the playing field between high-usage players and those with limited minutes, removing bias related to playing time or line assignments. That makes hidden strengths and costly inefficiencies far easier to uncover.

For a coach, this is the kind of chart that can challenge assumptions. You may notice a connection used far more often than you realized but producing little value. Or a low-volume pairing quietly generating significant aEC whenever it appears. That opens up strategic forks: if a high-usage connection isn’t dangerous, perhaps shift attention toward neutralizing the high-value ones. Conversely, if you find a pairing that the other team leans on heavily and produces above-average value, you can make stopping it a defensive priority.

Line Analysis

From there, the report moves beyond connections into line composition. Probable offensive lines are listed alongside other potential contributors, giving a clear picture of who’s most likely to take the field. This leads to what may be the most game-changing aspect: treating offensive and defensive lines as distinct units. This novel approach is not just for convenience or originality but because their personnel and therefore styles rarely overlap. The numbers back this up, offensive and defensive groups often have completely different tendencies. Recognizing these distinctions could be the difference between winning and losing in a tight game.

Within this framework, the metrics group into several categories: offensive and defensive efficiency, red-zone performance, hucking tendencies, and pace of play. Models from shownspace.com further refine the numbers people are accustomed to seeing. These include conversion rates over expected, which adjust raw values for field position and opponent strength; red-zone defensive efficiency on both sides of the disc; pace-of-play metrics capturing both time and passes per possession; and hucking frequency normalized to possessions rather than raw totals.

The value of this perspective immediately becomes clear in real examples. The DC Breeze, often labeled a small-ball team that rarely hucks, do fit that mold! But their O-line actually threw one more huck all season than the Spiders’ O-line (71 vs. 70). The bigger difference is in their D-lines: the Spiders hucked almost twice as often (52 vs. 29). The Glory flip the script entirely: their O-line launches over 37 hucks per 100 possessions, nearly double their D-line’s rate. On the other hand, the Spiders’ and Wind Chill’s D-lines huck more frequently than their O-lines, an inversion worth preparing for.

Pace of play gives us another useful narrative among those same teams. The Breeze run one of the league’s slowest tempos on both offense and defense (above the 90th percentile) yet so do the Glory, despite hucking at a far higher rate. The likely difference: the Breeze grind downfield with short passes, while the Glory bide their time until a deep look opens. Numbers like these don’t just describe what happened, they point to why and how to respond.

Red zone metrics reveal even more insights. The Flyers’ D -line converted 5 percent fewer red zone chances than their O-line. The Growlers’ O-line stopped opponents in the red zone 35 percent of the time, compared to just 7 percent for the Sol’s O-line defense. Those gaps matter when deciding whether to call a timeout for a potential break. Add in full field O-line defensive efficiency over expected, 26 percent for the Union versus 0.5 percent for the Wind Chill, should have a large sway in swinging the decision on whether to keep your D-line in after a turnover. This kind of data-driven thinking turns what might once have been guesswork into a simple comparison: how does your D-line offense stack up against their O-line defense next to what the matchup would be after a timeout?

With these metrics in hand, you can move beyond instinct and quantify the trade-offs in your strategic calls, whether you’re reshaping matchups, adjusting tempo, or deciding who should be on the field in the game’s biggest moments.

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Player Cards

Player cards condense a player’s impact into a single, data-rich view giving coaches, analysts, and players rapid access to insights. At the top of each card is a set of lollipop graphs showing:

· Contribution as shown by aEC for overall value

· Box score statistics for involvement in scoring plays

· Thrower profile metrics: Completion Percentage, Expected Completion Percentage (xCP), and Completion Percentage Over Expectation (CPOE)

Thrower profiles are particularly revealing. CPOE quantifies how a thrower performs relative to statistical expectation, allowing quick assessments of risk profile, volume, and efficiency. Coaches can adapt defensive strategies accordingly: against a low-risk thrower, a team might allow more open space, confident they will not force difficult completions. Against a high-variance thrower, defenders can target primary throwing lanes to pressure looks they are likely to throw anyway. Offensively, knowing a teammate’s CPOE can inform high-pressure decisions, such as selecting the right initiator during double-game point or red-zone possessions.

Beyond single metrics, player cards map throwing and receiving tendencies through connection diagrams and radar charts. These visuals reveal preferred throwing and receiving partners, the frequency of handler-to-handler interactions, and downfield targeting habits. The relative radar charts add another layer by showing deviations from league-average behavior, where the size of the bar reflects how often that throwing or receiving angle is used compared to the league norm rather than the absolute number of attempts. Gray bars indicate below-average usage, while blue bars indicate above-average usage. From a scouting perspective, this means you’re not just seeing what a player does, but rather you’re seeing how their habits stand out from the rest of the league, helping you identify which throwing lanes or receiving zones they’re most comfortable exploiting and which ones they may avoid.

Connection sankey diagrams visualize how frequently a player receives passes from or throws to specific teammates. They highlight preferred connections and targeting tendencies, making them a valuable tool for coaches looking to understand player habits and decision-making. For instance, a handler with unusually high swing-to-handler rates may fall back on resets under pressure, while a cutter with elevated deep-throw tendencies might attempt more hucks when faced with tight coverage. It could also reveal players who typically receive from handlers but distribute to cutters, or possibly the reverse.

Another layer of analysis comes from the inclusion of line-level data. Player cards display the top lines a player appears on and measure their contributions in those contexts. This makes it possible to predict role shifts based on surrounding personnel. If a high-contribution cutter’s value drops without a particular thrower, a defense can focus on separating that pairing. If a role player’s involvement spikes when paired with certain handlers, a coach can prepare countermeasures before the first pull. On the offensive side, teams can replicate high-performing personnel groupings by tracking how individual metrics vary across different lines.

In practice, this level of detail transforms preparation. A player receiving their primary defensive assignments can, within minutes, see each matchup’s role on the O-line, their aEC, their most dangerous throwing windows, and the situations in which they are most likely to attack. They will know whether to deny resets, anticipate break throws, or stay tighter when their matchup’s preferred thrower is in power position. They can even anticipate how that player’s role might shift if a key teammate is absent from the line.

The design is fully interactive, linking metrics, connections and radars. From a high-level roster prediction, a coach or player can drill into a player’s tendencies, evaluate their impact, and return to the broader view without breaking the flow. This makes it possible to build and adjust a game plan quickly.

Pulls

Finally, the report zooms in on the team’s top pullers and their tendencies, providing a detailed look at how they shape game flow. It breaks down how often each player keeps pulls in bounds, their frequency of roller throws, and maps out the most common pull locations with a heat map. It’s easy to spot if a team relies heavily on a particular player to throw rollers and which sideline they tend to target. On the flip side, you can analyze your own pullers to see who consistently puts the disc deep and in bounds, helping you optimize your own field position and counter opponents effectively.

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How to Use It

The Scouting Report is designed to deliver actionable insights efficiently, helping coaches, analysts, and players make data-driven decisions with confidence. Here’s how to get the most from it:

1. Review Predicted Rosters
Start by examining the predicted lineup to identify who is most likely to take the field and in what roles. This provides a head start in anticipating key matchups and identifying shifts in player roles that may not be readily apparent.

2. Explore Team Level Metrics
Dive into the offensive and defensive line metrics to uncover specific strengths and weaknesses on both sides of the disc. Treating these units separately provides a more nuanced view for targeted game planning and strategic adjustments.

3. Analyze Player Cards
Use player cards to gain a detailed snapshot of individual impact and tendencies. Advanced metrics and connection maps reveal who drives performance, who might be overlooked, and where mismatches could emerge.

4. Study Pulling Tendencies
Examine the pulling data to understand who handles pulls, their typical placement, and style. Anticipating opponent pull strategies lets you adjust positioning and get out of pressure situations.

5. Create Matchups and Tactical Focus
Use the report to identify which opponents require extra attention and where to apply pressure. Communicate these matchups clearly to players, while also spotting opportunities to provide help defense or allow some players to play more freely.

Beyond scouting opponents, this report is equally valuable for analyzing your own team. By uncovering your team’s patterns, strengths, and areas for improvement, you can identify opportunities to be more dynamic on offense and tighten up defensive schemes. Imagine approaching practice with a clear sense of which connections to encourage, which players to develop into key contributors, and how to adjust tactics based on the data.

By combining advanced analytics with a player-focused approach, this scouting report aims to contribute to the evolving landscape of Ultimate Frisbee strategy.

We’re currently developing a team page on shownspace.com that will feature some of this information, although the report itself and much of the competitive information will not be publicly available. If you’re interested in a scouting report tailored to your team or your opponents, we’d be happy to work with you to deliver these insights directly.