How it works
01. It ignores the box score noise
If a quarterback throws for 400 yards, but half of them came from tipped hail marys, you wouldn't expect a repeat performance. The algorithm does the exact same math for baseball. It ignores surface-level luck and looks strictly at contact quality and plate discipline—the physics of the play—to predict what a player will do tomorrow, rather than what luck gave them yesterday.
02. The gap is your trade signal
When a player's underlying expected metrics disagree with their actual stats, that disagreement is an actionable trade signal. Buy the player whose expected stats are better than their actuals. Sell the player whose underlying numbers are worse. The table shows you the exact differentials (in green and red) so you can spot positive and negative regression candidates at a glance.
03. History anchors the score
While the algorithm doesn't care about yesterday's luck, it does care about track record. A two-year historical baseline anchors every player's score. A proven veteran going through a two-week slump will hold their value, while a waiver-wire hero with no track record has to prove their underlying contact quality is elite before the algorithm fully trusts them.
04. The math is fully transparent
Skeptical that an unknown rookie is ranked higher than a consensus superstar? Click the exp link next to any Trade Value score. A diagnostic modal will pop up showing you the exact metric-by-metric breakdown, the expected vs. actual differentials, and the plain-English reasoning proving exactly why the algorithm scored them that way.
Baseline assumptions & limitations
- Standard 5x5 Roto Baseline: The algorithm assumes standard 5x5 categories. Saves are weighted heavily for closers, while Holds are treated as a secondary high-leverage metric. Adjust accordingly for your specific league settings.
- Injuries: The tool does not know about IL stints, pitch counts, or workload limits. A high score for a player who just tore their UCL is a fact about their contact quality, not a bug in the code. Context is yours to layer in.
- Early season: Before ~150 PA or 40 IP, scores lean heavily on historical baselines. By mid-May the current-season signal takes over. That's when the buy/sell calls are sharpest.
- Baseball knowledge required: This is a sophisticated assistant built for serious managers. A high Trade Value is a starting point for your decision, not the whole decision.
About early season scores
This tool is designed to function as an absolute trade calculator. If a player has a score of 45 and two players combine for 50, the math says take the deal. That comparison works well when the underlying data is trustworthy. Three weeks into the season, it is only partially trustworthy. Here is why, and what the scores are still good for in the meantime.
The small sample problem
Every score is built on z-scores, which measure where a player ranks relative to the rest of the league on each metric. At 60 plate appearances, those rankings are partially noise. A player hitting .380 with 6 home runs in April is generating elite z-scores on HR, RBI, xSLG, and wRC+, but those numbers come from maybe 55 balls in play. Contact quality metrics like Barrel%, HardHit%, and Exit Velocity stabilize faster than counting stats, but even they need more sample before they fully settle. The algorithm applies a PA-based dampener to early-season scores to account for this, but no formula fully compensates for data that simply has not accumulated yet.
How history factors in, now and later
In April, scores weight historical performance heavily. A player like Aaron Judge scores well right now not because of 61 plate appearances, but because his two-year historical profile is extraordinary and his early contact quality supports more of the same. A hot-start player with a thin track record scores lower than his surface stats suggest, because the algorithm treats small current samples with skepticism when there is no historical backing behind them.
This does not change in June. Historical performance continues to anchor scores throughout the season. What changes is how much weight the current season carries. By mid-summer, with 250+ plate appearances, the current-season signal is strong enough to stand on its own alongside the historical baseline. A player who is genuinely having a career year will see that reflected clearly. A player who was hot in April and has since cooled off will see his score adjust accordingly. The historical component never disappears. It just becomes one input among several, with the current season increasingly doing the heavy lifting.
Why scores converge early on
It definitely feels jarring to see a late round flyer matched up against a consensus superstar. The reason they sit at the exact same score comes down to the extreme polarization of their current stats colliding with the April sample size.
Superstars have huge historical bonuses anchoring them. If their current expected stats are struggling so much that they take negative metric penalties, their true value drops. The public market still values them highly, making them prime sell-high candidates. The math suggests trading them away on their name reputation before the box scores fully reflect their poor contact quality.
Conversely, a late round flyer might have almost zero historical padding. If their current expected stats are so overwhelmingly elite that positive metric bonuses are entirely driving their score, they become a massive buy-low target. The current performance gap between the two is so extraordinarily wide that it mathematically bridges the historical track record gap.
What you can reliably use right now
April scores are useful for identifying which hot-start players have contact quality that actually backs up what their stat line says. A player posting a 1.100 OPS on a .455 BABIP with soft exit velocity and a low Barrel% is a very different situation than a player posting the same OPS on an exit velocity of 97 mph and a 24% Barrel rate. The score reflects that difference, and the full breakdown behind every Trade Value number shows you exactly which metrics are driving it and which are dragging it down.
To see that breakdown, click the exp link next to any Trade Value score. It shows the base score, metric bonuses, combo bonus, historical bonus, and confidence multiplier, with the underlying z-scores for each stat. If a hot-start player's score seems high, the breakdown will tell you whether it is being earned by contact quality or inflated by counting stats on a thin sample. If a slow-start veteran seems undervalued, it will show you whether his historical profile is holding his score up. That context is what separates a useful early-season number from a misleading one.
When the scores sharpen
Once most qualified hitters have crossed roughly 150 plate appearances and starters have logged around 40 innings, the current-season signal carries enough weight to stand alongside the historical baseline. That tends to happen around mid-May for full-time players, though it varies depending on playing time and injury. At that point the PA-based dampeners fade out, the blending between current and historical performance stabilizes, and trade comparisons become reliable enough to act on directly. Check back as the season builds.