How does Pikola decide who to draft? By the weeks each player wins you.

Most managers draft from a list

Most managers draft from a list: Yahoo's draft order, an expert's ranking, or a cheat sheet. The best cheat sheets are built from projections. They add up how far above average each player is in each of the nine categories, and rank players by the total. It's a sound way to sort players, and it's what Pikola has to beat.

But a list can't see your draft

A list gives every manager the same ranking, and it never changes during the draft. Three things it can't see change what a player is worth to you.

• Some categories decide weeks more than others. A big edge in steals is often wiped out by one quiet week. An edge in blocks holds up better.
• Your team changes what you need. If you already win rebounds most weeks, another rebounder adds little. If you've all but lost threes, another shooter adds little too.
• The other managers decide the price. In an auction, what a player costs depends on the money left and how your room spends. In a snake draft, a player who'll still be there next round isn't worth your pick now.

So the question is whether a draft tool that sees those three things wins more. Pikola is built to answer it.

How Pikola thinks about a pick

Every time you're on the clock, or a player is up for bid, Pikola runs the same four steps (Exhibit 1). The first two are the same in every draft. The last two depend on whether you're in an auction or a snake draft. After every pick or sale, it starts again with the draft as it now stands.

To make the steps concrete, Exhibit 1 follows one pick through them: yours, at pick 50 of a 14-team snake draft where you pick seventh and every other team drafts by Yahoo's order. You've used your first three picks on Scottie Barnes, Domantas Sabonis and Jalen Duren, all eligible at centre, and your next pick is 63.

Exhibit 1How Pikola works, in an auction and in a snake draft

The running example, pick 50, through the four steps. Steps 1 and 2 are the same in every draft; steps 3 and 4 depend on the kind. The snake lane carries the example to Pikola's pick; the auction lane shows what it would ask instead. The numbers are Pikola's own. After every pick or sale, all of it runs again.

Mostly yours (over 70%)Live raceGiving up (under 30%)

Source: Pikola's engine, run on this draft at pick 50. The chances are its model of the week against the other thirteen rosters; "adds" is Pikola's value for a player on this roster, in points of weekly win chance, counted from an empty spot, so compare the two rather than reading either on its own; the chance of lasting to pick 63 is the share of its 200 play-outs of the room's next 12 picks. The room drafts by Yahoo's order, and "usually pick" is that order. The auction lane's two steps are described, not computed, because the example is a snake draft.

The rest of this page walks through that pick one step at a time, and asks of each step whether it earns its place.

Step 1: Picture your weeks. Some categories win more of them than others

Pikola starts by picturing a week in your league: who you'd start, how many games they'd play, and how each of the nine categories is likely to go against each team. That gives your chance of winning each category, and the week.

This is where it parts ways with a cheat sheet. A cheat sheet counts an edge in any category the same. Pikola counts it by how much it actually changes your chance of winning a week, and those aren't equal.

Exhibit 2One standard edge against a week's swing, category by category

For each category: how much the margin between two evenly matched teams swings from week to week (the faint bar), what one standard edge adds to it (the dark bar), and your chance of winning the category that week with that edge. One standard edge is the typical gap between two drafted players' weekly output. A 14-team Yahoo league.

A week's swing in the marginOne standard edge

Source: computed by Pikola's engine, not a measured result. A 14-team Yahoo league with the nine standard categories and Yahoo's default roster, from this season's projections. The swing is the standard deviation of the weekly margin between two average teams in the engine's model of a week; the edge is one standard deviation of players' weekly contributions across the drafted pool, the z-score unit a cheat sheet counts in; the chance is the engine's own, with ties counted for the whole-number categories.

Read it like this: the faint bar is how much the margin between two evenly matched teams swings from one week to the next. The dark bar is what one standard edge adds, the typical gap between two drafted players. In blocks, that edge is a fifth of the swing, and it lifts your chance of winning blocks that week from 50% to about 58%. In steals it's a tenth of the swing, and lifts your chance only to about 54%. The same-sized edge, about half the payoff. A cheat sheet counts the two dark bars the same.

The exact numbers depend on your league's size and how its teams are built, and Pikola works them out for yours. Apart from turnovers, steals counted least in every league size we checked, from 10 to 16 teams, and the two percentages always counted below average. Turnovers count half by design: of the rules we tried in simulated drafts, it was the best or tied for best in every kind of league, most likely because the busy players who commit turnovers are the same ones who win points, threes and assists.

So what: don't pay list price for a player whose value is mostly steals or shooting percentages. On its own, that won't beat a good cheat sheet. It pays off once you also ask what your team needs, which is step 2.

Step 2: Ask what each player adds to your team, again after every pick

Next, Pikola asks how much each player still on the board would raise your chance of winning the week, with the team you have. Your team changes with every pick, so the answer does too. Here's what three centres did to yours.

Exhibit 3Three centres in: where each race now stands

Your chance of winning each category at pick 50 of a 14-team snake draft, with three picks straight off Yahoo's order (hollow) and with the three centres instead (filled), one row per category, grouped by where the race now stands. Under 30%, Pikola calls a category given up; over 70%, it's mostly yours; the live races are between. "Counts" is how much more or less Pikola now weighs each category.

BeforeLive raceGiving upMostly yours

Source: two drafts run in Pikola's engine from the seventh seat of a 14-team league, every other seat taking the best player left by Yahoo's order, each read at the seat's fourth pick: in one the seat takes the best player left by that order for three rounds, in the other the best centre left, Scottie Barnes, Domantas Sabonis and Jalen Duren. The chances are the engine's model of a week against the other thirteen rosters, with every team's open spots filled by an average pick. "Counts" is the change in the engine's weight on each category between the two drafts.

The three centres changed the races. Rebounds went from a little under even to about three weeks in four, and field-goal percentage to about seven in ten. Threes fell from about two weeks in three to one in seven, and points from about even to under three in ten. Assists rose from one week in four to about three in eight.

So Pikola now counts threes about a quarter less, because they're slipping out of reach, and rebounds about a quarter less too, because they're all but won. Assists, the race the three centres left most open, now count a third more. There's no switch that turns a category off. As a race drifts out of reach, or is all but won, one more unit of it moves your week less, so Pikola counts it less, and the weight shifts to the races still live. That's how a punt finds you instead of being planned. Pikola will let two categories drift away like that (three in a one-win league, where the week goes to whoever wins more categories); if a third falls under a 30% chance, it pulls the other way and protects it. Points, just under that line at 29%, still count a little more than before: a scorer still moves that race, and with two categories all but won, the weight has to go somewhere. Pikola's weights are shares of a fixed total, so when threes and rebounds fall, the rest must rise. In our punt study, no punt planned before pick one won more titles than staying flexible, and most clearly cost some.

Does re-checking after every pick matter? We tested it in simulated snake drafts against a league of cheat-sheet drafters.

Exhibit 4Extra categories won per 100, against a cheat-sheet drafter

Simulated 14-team snake drafts in a league of cheat-sheet drafters, 336 seasons for each way of drafting, measured on 28 September 2026. A hollow bar is so close to zero that it could just be luck.

Extra categories won per 100Hollow: could be luck

Source: simulated 14-team snake drafts, 14 seats × 3 league draws × 8 seasons = 336 seasons per way of drafting, the other 13 seats drafting by a z-score cheat sheet. Paired: each way of drafting took the same seat in the same league. Method review of 28 September 2026. The hollow bar is within two standard errors of zero.

Read it like this: weighting the categories by the weeks they win, but the same way for every team, made no difference: those teams were no better than a cheat sheet's. Re-checking after every pick is what made the difference: it won about 1.4 more categories in every 100, and 2.7 with turnovers counted at half. That's roughly one extra category won every four weeks, from the draft alone.

How much the re-check adds depends on who you're drafting against. In a later, larger test in 16-team leagues drafting by Yahoo's order, counting the weeks alone already put Pikola about 9 categories per 100 ahead of those leagues' picks, and re-checking added nothing we could measure on top. Where every other team drafted by Pikola's weights, re-checking added about 0.4 more, right at the edge of what we can tell from luck. Against a list-follower, counting the weeks does most of the work; the re-check most likely matters more as your league gets sharper, but we haven't measured that cleanly.

So what: re-rank after every pick. The best player on the list is often not the best player for your team, and the gap grows as your team fills up.

Steps 3 and 4: Price in the other managers

The last two steps bring in the rest of your draft room, and they work differently in the two kinds of draft.

In an auction, step 3 works out what each player will likely cost. Pikola starts from what real drafts pay: four seasons of a real Yahoo league's auctions, scaled to your league's size and budget, or your own league's past drafts if you paste them into Setup, blended with Yahoo's own prices. After every sale it re-prices everyone. The money left in the room is shared out by value, so if the room overspends on stars, everyone else gets cheaper. It also learns how your room bids: whether stars are going for extra, how fast the money is going, and how hard each manager pushes.

Step 4 sets your top bid: what the player adds to your team, checked against what the same money would buy you elsewhere in this draft. Early on, with most of the draft still to come, that check is rough, so it gets little say. It gets more as your team fills up. So your top bid for a player can sit above what the room would pay, when you need what that player does, or below it, when you don't.

Here is that starting point for a 14-team, $200 league: the top player at about $81, the top 42 picks, three per team, taking about 60% of the room's $2,800, and 34 players going for $1. Every sale re-deals this curve over the money that's left.

Exhibit 5What the room is expected to pay, player by player

Pikola's expected price for each of the 182 players a 14-team, $200 league drafts, best first, before the first sale.

Source: computed by Pikola's engine for a 14-team, $200 Yahoo league: four seasons of a real league's auction results, each season's prices by rank scaled to this league's size and budget and averaged, blended with Yahoo's projected and average prices, and adding up to the room's $2,800. Prices by rank only; no player is named.

In a snake draft, step 3 works out who will still be there at your next pick. Pikola treats each player's draft spot as a guess around where your room usually takes them: give or take a pick at the top of the draft, about 21 picks either way around pick 100. That spread was fitted to a real 14-team league's 69 picks. On those same picks, of the players it expected to be gone before pick 70, it said 7.0% would still be there, and 7.2% were. That checks the fit; it hasn't been tested on another league's draft yet. Then Pikola plays out the room's 12 picks before your turn 200 times, like practice runs, and counts how often each player is still there.

Step 4 makes the pick: the best player now, with your next pick in view. In our example, your next pick is 13 picks away, at pick 63, and the room makes 12 picks before it. Desmond Bane, usually taken around pick 53, is still on the board and adds most to your team, but lasts to 63 in only 39% of Pikola's play-outs. Darius Garland, usually taken around 64, adds nearly as much and is still there 65% of the time (Exhibit 6); Dejounte Murray, usually around 70, a little less and 79%. On a list, Bane sits 0.6 ahead of Garland. For your team, with threes slipping away and assists the open race, the gap is 0.1. So Pikola takes Bane now and plans on Garland or Murray at 63. Here the look ahead agrees with the simple pick: Bane adds most and is also the least likely to come back. It changes a pick only when the player who adds most would still be there for you and the runner-up wouldn't. At the turn, where your two picks come back to back, it weighs both together.

Exhibit 6Who'll still be there at your next pick

The running example: it's pick 50 and you pick again at 63. Each curve is where the room tends to take that player, from Yahoo's order and the fitted spread; the part before pick 50 didn't happen, so it's faint. The shaded part is past 63, when the player is still there for you. The percentages are from Pikola's 200 play-outs of the room's 12 picks.

Desmond Bane, usually around pick 53Darius Garland, usually around pick 64

Source: Pikola's own survival model at its default spread, 1 + 0.2 × the player's usual pick: a normal around the room's order, cut off at the current pick. The percentages are the share of the engine's 200 play-outs of the room's 12 picks in which each player is still there at 63; the curves' own tails give 33% and 64%, a little lower, because the play-outs also count that only 12 players go. Computed, not measured; the spread was fitted to one real league's draft.

Which of these steps matter most? When we switched parts of Pikola off one at a time in simulated auctions, two clearly mattered: knowing how real drafts spend, worth about two extra categories in every 100, and picturing the ten players you actually start each week, worth about the same.

So what: in an auction, know how your room spends, not just what a player is worth. In a snake draft, when two players are close, take the one who won't make it back. Our simulated leagues couldn't measure a gain from that, so treat it as a tiebreak.

Does it work? It wins more titles than the common ways of drafting

The tests are paired: Pikola in one seat and a common way of drafting in another, the same league, the same players and the same luck, so any gap comes from how they draft. The full method for the auction tests is in How Pikola is tested.

• +18 to +29titles per 100 seasons over a manager drafting by Yahoo's order, in snake drafts
• 3×the titles of a disciplined cheat-sheet drafter, in auctions
• 7 of 7kinds of league where Pikola came out ahead

Exhibit 7Extra titles per 100 seasons for Pikola

Snake: 16-team leagues, against a manager drafting by Yahoo's order in the same seat, 128 drafts of 8 seasons in each kind of league. Auction: 14-team leagues, against a disciplined cheat-sheet drafter in the same auction, 40 auctions of 32 seasons in each.

Snake draftsAuctions

Source: snake, the pre-registered 16-team test: 16 seats × 8 league draws = 128 paired drafts, 8 seasons each, seasons played on the projections, Pikola against a manager drafting by Yahoo's order in the same seat. Auction: 40 fourteen-team auctions × 32 seasons per kind of league, Pikola and a cheat-sheet drafter in the same auction, 2 October 2026. Every bar is more than two standard errors above zero.

Read it like this: in snake leagues, Pikola won 18 to 29 more titles per 100 seasons than a manager following Yahoo's order, even in a league where every other team drafted by Pikola's own weights. In auctions it won about three times the titles of a disciplined cheat sheet, about 26 against 9. For scale, an average team wins about 7 titles per 100 seasons in a 14-team league, and about 6 in a 16-team one. When the same snake drafts were scored on projections 15% off, or in leagues that knew more than the projections, the gap ranged from about 5 to 31 titles per 100 seasons, and never closed.

So what: drafting from a fixed list, whether Yahoo's order or a cheat sheet, left titles on the table in every kind of league we built, including one where everyone else drafted by Pikola's weights.

What this means on draft day

• Count weeks, not rank. Ask what a player does to your weekly races, not where they sit on a list.
• Discount the streaky categories. Steals and shooting percentages win fewer weeks than their rankings suggest.
• Re-rank after every pick. Let races you've won or lost go, and chase the live ones. That's how a punt should find you.
• Price in the other managers. In an auction, how your room spends and the money left matter as much as the player. In a snake draft, when two players are close, take the one who won't make it back.

The methods, for readers who want them

Each step above rests on a method chosen for a reason. Here they are, named, with the reason, for readers who want them. The full model follows, folded.

1. A week's totals. Method: each category's weekly total is a normal distribution, built from per-game projections, expected games played, and the ten lineup slots your league starts, so a bench player counts only in the weeks they'd start. Percentages come from makes and attempts. Why: the question is how often your total beats theirs, which needs the spread of a week as well as its average. A sum of thirteen players' season totals answers a different question.
2. Winning a category. Method: the chance one team's total beats the other's, with ties counted for the whole-number categories. Why: steals, blocks and threes tie often in a real week. Ignoring ties overstates how often an edge wins.
3. Winning the week. Method: in a categories league, where each category is its own win or loss, the expected share of the nine categories won. In a one-win league, the chance of winning five or more, combining all nine chances at once (a Poisson-binomial distribution). Each opponent is scored separately and the results averaged. Why: averaging the opponents first would say you're about even against everyone. In fact you're favoured against weak teams and not against strong ones, and the week's result depends on which you're playing.
4. The playoffs. Method: playoff weeks get their own weight, 35% of the total, as single matchups against the strongest teams in the league. Why: a title is three knockout weeks against good teams. A model of the regular season alone would undervalue what wins those. This weight is one we've marked for re-testing.
5. What a player adds. Method: the slope of your chance of winning with respect to each category (the gradient), times the player's weekly contribution in each. Your open roster spots, and every opponent's, are stood in by the average player of each rank band of what's left. Why: one player is a small change to a team, and a slope prices small changes in the right currency, your chance of winning. Filling open spots with an average pick keeps the answer from jumping when one assumed player changes.
6. Giving up a category. Method: a threshold and a cap. A category under a 30% chance is treated as given up, and the model may give up two (three in a one-win league) before it protects the next. Why: the cap was measured, and raised titles. The 30% line is a judgement, not a measurement, and the limits above say so.
7. Auction prices. Method: a price ladder that always adds up to the money left in the room. Its starting point is a price-by-rank curve from past drafts, scaled to your league, blended with Yahoo's prices. From the sales, a Bayesian update learns the room's star premium, its pace and each manager's aggressiveness, under tight priors. Why: prices in a room must add up to the money in it. That one rule produces late-draft inflation and the pressure of unspent cash without anything being learned. Tight priors mean one odd sale moves little.
8. Your top bid. Method: what the player adds, checked by a beam search over market cells (player types × price tiers × positions) that compares the best rosters you could finish with and without the player. The price scenarios are drawn once and shared across every comparison (common random numbers, in a Latin hypercube). The search's say grows as your roster fills. Why: searching over named players overfits to players who'll be sniped; cells don't. Sharing the price draws keeps the difference between two searches from being noise. With little bought, the search is noisy, so it's heard less.
9. Who survives to your next pick. Method: each player's pick is a normal around the room's order, with a spread of 1 + 0.2 × position, fitted by maximum likelihood to one real league's draft. The picks before your turn are simulated 200 times, with the same draws shared across every candidate, and the spread's scale learns from this draft's reaches under a prior worth 40 picks. Why: shared draws make "with this player" and "without" comparable. The prior stops one early reach from rewriting the model.
10. Testing. Method: paired simulation experiments. Both drafters take the same seat in the same league with the same random luck, so only the drafting rule differs. Standard errors are taken across the paired drafts, a gap counts only when it's more than twice its standard error, and the main snake test's sample size and rule were set before it ran. Parts are switched off one at a time, and the same drafts are rescored on projections 15% off and on a league that knows more than the projections. Why: pairing removes most of the luck between the two drafters. The two-error rule and the pre-set sample stop us reading noise as a result.

The model in full

Step 1: a week

Each team's week is modelled from its players' per-game projections: ten starters by the league's roster slots, with the bench and the waiver wire filling in for missed games where they can. A projection's games are read halfway toward 70 (66 as 68, 82 as 76). A projection of 30 games or fewer is a known long absence and is read as itself; the halfway rule fades in between 30 and 50, so 40 is read as about 48 and 50 as 60. Every projection then loses three games, and none is read below 20 unless it already was. Missed games are split into the kind a bench covers and the kind that is simply lost. Each category's weekly total is treated as roughly normal, with a variance that includes who happens to start; the percentages are modelled from makes and attempts, so a high-volume shooter moves them more.

Against each opponent, the chance of winning a category comes from the gap between the two weekly totals and its spread, with ties for the counting categories. In a categories league a week is worth the expected share of the nine won; in a one-win league, the chance of winning five or more, from all nine chances at once. Each opponent is scored separately and the results averaged, never the other way round: against a weak team you're favoured almost everywhere, against a strong one almost nowhere, and averaging the opponents first would lose that. The playoff weeks, single matchups against the strongest teams, get their own weight in the total. Turnovers count half: their chance of being won is pulled halfway toward a coin flip before it's counted.

Step 2: what a player adds

A player's worth to your team is their weekly contribution in each category times how much one more unit of that category raises your chance of winning (the gradient), at your team as it stands. Your open spots are filled with the average player of each band of what's left, the way an average draft would fill them, and the other teams' rosters are filled the same way. With nothing drafted, every team looks the same and the weights are the league's; as your picks fill the roster, they outweigh the fill and the weights bend to them. A category is treated as given up when you name it, or when your chance of winning it falls under 30%. Left to itself, the model may give up two categories, three in a one-win league, before the next one is protected.

Steps 3 and 4 in an auction

Worth becomes dollars on a ladder: the money left in the room, less a dollar for each open spot, shared out in proportion to worth above the last player a team would keep (the two spots each team streams from the wire go for a dollar). The room's expected prices always add up to the money it has left, which is what drives late-draft inflation and the pressure of cash with nowhere to go. The prices start from past drafts (a real 14-team league's four seasons, or your league's if you paste them), the k-th best player at the k-th highest price, scaled to your league's size and budget, blended with Yahoo's projected and average prices, and they learn this room's star premium, pace and each manager's aggressiveness from its sales, under tight priors so one strange sale moves little. Your top bid starts from what the player adds to your team. As your roster fills, a search through the rest of the draft, comparing the best rosters you could finish with and without that player, is given a growing share of the say.

Steps 3 and 4 in a snake draft

The pick is the best legal player by what they add to your team, with your next pick in view. Each player's draft position is a normal around the room's order (Yahoo's expert ranking where the room shows it, then the room's average draft position) with a spread of 1 + 0.2 times that position in picks, fitted to a real 14-team league's picks (the best fit was 1.0 + 0.18 times). The picks before your next turn are drawn 200 times and shared across every candidate, so a candidate's worth with the next pick in view is their own plus the best of who survives. The spread's scale learns from how far this room reaches, under a tight prior. At the turn, the two picks are weighed together.

The numbers behind the exhibits, and the parts switched off

Where	What was measured	Result	Sample	When
Exhibit 4	Same for every team (never re-checked), extra categories won per 100 against a cheat sheet	−0.7 ± 0.4	14-team snake, 336 seasons	28 Sep 2026
Exhibit 4	Re-checked for your team (after every pick), extra categories won per 100 against a cheat sheet	+1.4 ± 0.3	14-team snake, 336 seasons	28 Sep 2026
Exhibit 4	Pikola (re-checked, turnovers at half), extra categories won per 100 against a cheat sheet	+2.7 ± 0.4	14-team snake, 336 seasons	28 Sep 2026
Parts switched off	How real drafts spend (past drafts' prices, by rank), what it adds to the categories Pikola wins per 100	+2.2 ± 0.6	16-team auctions, 640 seasons	28 Sep 2026, earlier engine
Parts switched off	Weekly lineups (the ten you start each week), what it adds to the categories Pikola wins per 100	+1.8 ± 0.6	16-team auctions, 640 seasons	28 Sep 2026, earlier engine
Parts switched off	Prices add up (to the money left in the room), what it adds to the categories Pikola wins per 100	−0.5 ± 0.6	16-team auctions, 640 seasons	28 Sep 2026, earlier engine
Parts switched off	Each team scored apart (not one average opponent), what it adds to the categories Pikola wins per 100	−0.2 ± 0.5	16-team auctions, 640 seasons	28 Sep 2026, earlier engine
Parts switched off	Playoff weighting (extra weight on playoff weeks), what it adds to the categories Pikola wins per 100	−0.2 ± 0.5	16-team auctions, 640 seasons	28 Sep 2026, earlier engine
Parts switched off	How real rooms spend, what it adds to titles per 100 seasons over a cheat sheet	+7.3 ± 3.1	16-team auctions, 640 seasons	28 Sep 2026, earlier engine
Exhibit 7	Snake, League by Yahoo's order: titles per 100 seasons over a manager drafting by Yahoo's order (weekly category wins +9.2)	+28.9 ± 2.1	16-team snake, 1,024 seasons	recorded 2 Oct 2026
Exhibit 7	Snake, Yahoo's order, plus needs: titles per 100 seasons over a manager drafting by Yahoo's order (weekly category wins +9.2)	+24.6 ± 1.8	16-team snake, 1,024 seasons	recorded 2 Oct 2026
Exhibit 7	Snake, League by Pikola's weights: titles per 100 seasons over a manager drafting by Yahoo's order (weekly category wins +5.1)	+17.8 ± 2.0	16-team snake, 1,024 seasons	recorded 2 Oct 2026
Exhibit 7	Snake, A mix of all three: titles per 100 seasons over a manager drafting by Yahoo's order (weekly category wins +8.0)	+23.6 ± 1.8	16-team snake, 1,024 seasons	recorded 2 Oct 2026
Exhibit 7	Auction, Last year's prices: titles per 100 seasons, Pikola 26.4 against a cheat sheet 9.4	+17.0 ± 2.0	14-team auctions, 1,280 seasons	2 Oct 2026
Exhibit 7	Auction, A mix of both: titles per 100 seasons, Pikola 25.7 against a cheat sheet 9.6	+16.1 ± 1.6	14-team auctions, 1,280 seasons	2 Oct 2026
Exhibit 7	Auction, Yahoo's numbers: titles per 100 seasons, Pikola 26.6 against a cheat sheet 9.0	+17.6 ± 2.2	14-team auctions, 1,280 seasons	2 Oct 2026
Snake model	Players expected gone before pick 70: share predicted still there, against the share that was	7.0% against 7.2%	one real 14-team league, 69 picks	recorded 2 Oct 2026

Exhibits 1, 2, 3, 5 and 6 are computed by Pikola's engine for a standard 14-team Yahoo league (PG, SG, G, SF, PF, F, C, three Util and three bench; $200 in the auction) at its default settings, from this season's projections, as each source line says; the auction lane of Exhibit 1 describes its two steps rather than computing them. Exhibits 4 and 7 and the parts switched off are the measurements listed above, each from the run named. The auction tests use the engine of 2 October 2026; the part-by-part tests an earlier one.

Pikola is a Chrome extension that advises live in Yahoo fantasy basketball drafts, built on this model. Try it free in a mock draft · How Pikola is tested · More Draft Science