Scoring Shift

How it works

Scoring Shift is the portfolio's one predictive model, and it now covers all three leagues: a signed forecast of a player's forward production, up or down — points per game in the NBA, production per hundred trips to the plate in MLB, scrimmage yards per game in the NFL. Each league's model is its own build, and they did not all learn the same lesson from the same testing discipline.

In basketball, the evidence blends four pieces, each one tested on its own before it earned a place:

• Recent scoring above a player's own last six months tends to continue.
• Except the part that came purely from playing more minutes. That part fades, because minutes come back to earth.
• Accuracy far from the player's established level gets pulled home. Well above it, expect give-back. Well below it, expect recovery.
• A stretch still climbing inside itself keeps climbing. A player whose last five games are bigger than the fifteen before them is not finished.

In baseball, the forecast leans on the longest memory in the portfolio: a hitter or pitcher producing above the level their own play established more than a year ago tends to come back to it, and one producing below it tends to recover. The batter version also reads luck on balls in play against the player's own history. For a pitcher the number is oriented the way a fan thinks: positive means better run prevention ahead. And the forecast deliberately knows nothing about playing time — it speaks to what a player does per plate appearance, not how many they get.

In football, the same testing found the same headline even more sharply: recent production above a player's own established pace tends to give itself back. A running back or receiver surging past their trailing-year level is more likely to cool than to keep climbing, and a slumping one with an established level behind them is more likely to recover — so the forecast leans against the streak, not with it. Touchdowns are deliberately nowhere in it: TD rates are among the least stable numbers in football, and a forecast built on them would be modeling dice. And a promise kept: the quarterback version's test failed, so it does not exist, and the product says so rather than improvising.

The forecast is a lean, never a promise. Ranked by this number and checked against two full seasons each model had never seen, players rose and fell with the ranking far more reliably than chance — in all three leagues. It still explains a minority of what happens next, because most of a player's future is injuries, trades, and coaches, and no box score contains those. The difference it offers is between an informed lean and a coin flip, not between a lean and a crystal ball, and every answer built on it says so.

Reading the player models together

The two descriptive metrics sort a player into one of four stories, and the predictive one prices the story — in points, in production, or in yards.

• Changed role, real production. The strongest riser read: the job grew, and the scoring under it is what the process supports.
• Changed role, borrowed production. Believe the minutes, discount the points. The role is real; the scoring rate on top of it is partly on loan.
• Same role, borrowed production. The pure hot hand. Same job, more makes, and the fade is coming.
• Same role, real production. They are what they are, and it is real. Stable is an answer, not a shrug.

Scoring Shift is the bottom line under that grid. It weighs how large each force is, nets the ones pushing against each other, and hands back one number. Ask what happens next and the answer leads with the forecast; the other two metrics are the why underneath it.