After the first round of the NFL draft, the question fans most want answered is: How did my team do? It seems straightforward, but landing on a solid answer is tricky. Since the market for new NFL labor isn’t free or open, deciding whether a team got good value at a pick rests largely on assumptions about the rest of the league’s preferences. And NFL teams are notorious for keeping those preferences a secret, even after the draft is over.

While the true market for rookie players is probably unknowable, that doesn’t stop analysts and teams from trying to model it. One way to get a handle on things is to aggregate the mock drafts produced by the media. This year, ESPN’s Brian Burke did just that, and we wrote about a few of the insights gleaned from his model in the lead-up to the draft. Now with the first 32 picks in the books, we can see how the model fared and use it to get a sense of which players were taken earlier than expected — and which ones fell.

The first big reach of the night belonged to New York Giants GM Dave Gettleman. With the fourth pick of the draft, the Giants selected tackle Andrew Thomas of Georgia. But Burke’s model — which is based on the aforementioned mocks as well as team needs, scouting assessments and trade possibilities — gave this pick literally zero percent chance of happening. Just four picks into the draft and Gettleman had already broken the model (and he wasn’t the last GM to do it). How did this happen?

The move could be explained by a lack of trade partners for Gettleman. When a team likes a player more than the rest of the league does, the rational thing to do is to trade down and accumulate extra picks, gambling that the player you like will still be around later in the draft, near the area you traded to. But that outlet only exists if a team has willing trade partners. That hasn’t been much of a problem in past years, and high picks have been dealt frequently. In six of the previous 10 drafts, picks within the top four — right where the Giants found themselves — were involved in trades. But no picks in that range changed hands this year, perhaps owing to the unfamiliar nature of a virtual draft. In fact, for just the third time since 2010, NFL teams failed to consummate a trade of any kind with the first 10 picks.

So it’s plausible that drafting from home may have put a damper on trades, but it’s also possible that Gettleman is easy to read. The two teams picking directly behind the Giants — the Dolphins and the Chargers — selected quarterbacks immediately following New York’s pick. Since the Giants spent first-round capital on quarterback Daniel Jones last year, it’s likely that neither Miami nor Los Angeles felt a need to trade up, and both were comfortable with letting Gettleman have his choice of the rest of the draft class.

Whatever the reason, Gettleman selected Thomas three spots before Burke’s model thought anyone would even begin to consider him. Not to be outdone, the Las Vegas Raiders took cornerback Damon Arnette with the 19th pick, a full eight spots before the earliest slot that ESPN’s model predicted that Arnette could be chosen. And the Seahawks weren’t far behind, selecting linebacker Jordyn Brooks with the 27th pick, six spots ahead of his earliest projected draft slot.

Five first-round picks were probably reaches Players taken in the first round of the 2020 NFL draft with the probability that each player would have been chosen with an earlier pick, according to a model from ESPN Stats & Information Group Pick Player Team Pos Prob. taken prior 1 Joe Burrow Cincinnati QB 0.0% 2 Chase Young Washington DE 5.3 3 Jeff Okudah Detroit CB 4.6 4 Andrew Thomas N.Y. Giants OT 0.0 5 Tua Tagovailoa Miami QB 66.9 6 Justin Herbert L.A. Chargers QB 29.4 7 Derrick Brown Carolina DT 22.9 8 Isaiah Simmons Arizona OLB 79.1 9 C.J. Henderson Jacksonville CB 1.9 10 Jedrick Wills Cleveland OT 35.0 11 Mekhi Becton N.Y. Jets OT 66.9 12 Henry Ruggs Las Vegas WR 16.1 13 Tristan Wirfs Tampa Bay OT 97.2 14 Javon Kinlaw San Francisco DT 42.0 15 Jerry Jeudy Denver WR 76.8 16 A.J. Terrell Atlanta CB 0.5 17 CeeDee Lamb Dallas WR 87.3 18 Austin Jackson Miami OT 2.4 19 Damon Arnette Las Vegas CB 0.0 20 K’Lavon Chaisson Jacksonville EDGE 58.7 21 Jalen Reagor Philadelphia WR 0.0 22 Justin Jefferson Minnesota WR 58.4 23 Kenneth Murray L.A. Chargers LB 62.6 24 Cesar Ruiz New Orleans C 3.6 25 Brandon Aiyuk San Francisco WR 20.4 26 Jordan Love Green Bay QB 74.1 27 Jordyn Brooks Seattle LB 0.0 28 Patrick Queen Baltimore LB 64.1 29 Isaiah Wilson Tennesee OT 0.0 30 Noah Igbinoghene Miami CB 0.8 31 Jeff Gladney Minnesota CB 3.2 32 Clyde Edwards-Helaire Kansas City RB 0.3 Players in bold had a 0 percent chance of being taken before that pick. Source: ESPN Stats & Information Group

Value isn’t permanently destroyed in a draft — it’s just transferred to another team. For every overreach, there is another player who falls and becomes relatively inexpensive to acquire. Call it the law of conservation of value. A few teams took advantage of that surplus value in the first round, particularly those that were looking for a wide receiver.

Perhaps because this year’s class is widely regarded as one of the deepest at receiver in years, Jerry Jeudy and CeeDee Lamb both lasted longer than the ESPN model predicted was likely. Burke’s model estimated that Lamb would be gone by the 17th pick 87.3 percent of the time, which made him a smoking good deal for the Cowboys. And the Broncos got a bargain selecting Jeudy two spots earlier with the 15th pick, a draft slot at which the model predicted he would be available just 23.2 percent of the time.

But the biggest value of the first round was Tristan Wirfs, an offensive tackle selected by Tampa Bay to protect the recently signed Tom Brady. ESPN’s model gave Wirfs just a 2.8 percent chance of being available at pick No. 13, making the Iowa product the steal of the first round and Tampa Bay the toast of the 2020 draft.

CORRECTION (April 24, 2020, 6:52 p.m.): A previous version of the table in this article gave an incorrect probability of Jerry Jeudy being taken higher than the 15th pick. His odds were 76.8 percent, not 92.5 percent.