Every card in Legends Deck turns real MLB Statcast data into a fixed set of ratings, and understanding that pipeline is the only way to build a roster that wins simulations consistently.
Core mechanics
Legends Deck cards map to real MLB players. Every rating on a card originates from Statcast inputs: exit velocity, barrel rate, hard-hit rate, sprint speed, outfield arm strength, outs above average, spin rate, spin efficiency, and release extension. The game does not use subjective scouting grades. It converts observable batted-ball and pitch-tracking data into card attributes.
Batters receive contact, power, and fielding attributes. Contact derives from strikeout rate, whiff rate, and contact quality on swings. Power derives from exit velocity, barrel rate, and extra-base hit frequency. Fielding draws on sprint speed, OAA, and arm strength. Pitchers receive velocity and spin attributes. Velocity maps to fastball velocity and average pitch velocity. Spin maps to spin rate, spin efficiency, and movement profiles.
Those raw inputs are processed into a normalizedRating. The normalization scales each attribute against league averages and positional peers, so a high power rating means the player performs at that percentile relative to other players at the same position, not that they hit a specific absolute exit velocity. The formula weights inputs differently by position. For catchers, fielding and arm strength carry more weight in the composite than raw power. For starting pitchers, velocity and spin are weighted against innings and usage; for relievers, short-burst spin and whiff metrics dominate.
Rarity tiers separate cards into supply classes, affecting marketplace availability and set collection weight. Rarity does not change the simulation math—only the attributes and normalizedRating do. A Hall of Fame tier system, legendTier, sits on top of base rarity and marks cards with career-defining Statcast peaks or sustained elite production.
The simulation engine reads these final ratings to determine game outcomes. A batter’s contact rating influences whether a plate appearance ends in an out or contact; power rating influences extra-base outcomes; velocity and spin influence pitch results. Fielding ratings determine whether balls in play become hits or outs. No rating is decorative.
Step-by-step strategy
1. Identify the Statcast inputs behind a card before you acquire it. Review the player’s exit velocity, barrel rate, sprint speed, or spin rate over recent seasons. A card’s rating is only as durable as the data behind it.
2. Match attributes to roster role. Contact-heavy batters belong at the top of the order where plate appearances are most frequent. Power bats belong in run-producing spots. High-velocity, high-spin pitchers belong in high-leverage relief or starting roles depending on stamina and usage indicators.
3. Compare normalizedRating within position, not across the full card pool. A catcher with an elite fielding rating is the best option at catcher while an outfielder with the same number is merely average. Positional context decides value.
4. Weigh rarity tier and legendTier against simulation performance, not just prestige. A legendTier card carries historical weight, but if its underlying Statcast inputs have declined, a lower-rarity card from a breakout season outperforms it in the engine.
5. Build for attribute complementarity. Pair high-contact hitters with power hitters so pitchers cannot pitch around your entire lineup. Pair high-spin pitchers with defenders who convert ground balls into outs.
6. Time marketplace moves around set collection cycles and auction windows. Cards needed for active sets carry premiums outside their simulation value, so separate collecting goals from roster-building goals.
7. Check for Statcast regression before bidding. A card built on one outlier season carries a high normalizedRating that collapses when the next data update lands. Prefer players with stable or improving Statcast trends over single-season spikes.
Common mistakes
- Buying the highest normalizedRating without checking which attributes produce it. A high rating driven by power is useless if your lineup already lacks contact.
- Ignoring positional normalization. Comparing a shortstop’s rating to a first baseman’s rating misleads because the peer groups and weightings differ.
- Overpaying for legendTier cards on name recognition alone. The simulation uses current attributes, not career reputation.
- Stacking one attribute type. A roster of pure power batters with no contact strikes out too often; a rotation of pure velocity pitchers with no spin gives up hard contact.
- Assuming raw MLB Statcast leaderboard rank equals card rating. Normalization adjusts for league context, park effects, and era, so a league-average performer in a down offensive year rates higher than a star in an extreme environment.
Advanced angle
Find rating efficiency. The simulation weights certain attributes more heavily in specific situations. A reliever with elite spin but mediocre velocity rates higher than his raw stuff suggests because spin drives whiff rates in short appearances. A center fielder with elite sprint speed and OAA rates above his offensive contribution because defensive outs carry outsized weight in close games. A power hitter with a high barrel rate but low contact rating produces extra-base hits when he connects, but he needs contact protection in the lineup. Target players whose normalizedRating is inflated by one simulation-critical attribute rather than overall stardom, and roster around that attribute’s weakness.
Related reading
Try it: play a half-inning
See the Statcast engine in action. A loaner lineup against a CPU pitcher, three outs or three runs, about 30 seconds, no signup.