The Mets Aren't Losing Lindor — They're Losing Everyone
New York's worst-in-baseball start has a clean injury explanation, but it began three weeks before Francisco Lindor went down. The cascade is the story; Lindor is just the most recognisable name in it.
Sixty-five.
That is the cumulative number of New York Mets starts that have been made by someone other than the player penned into that lineup slot at the start of the season. Across seven distinct injured-list stints since opening week, six different MLB-relevant players have spent meaningful time unavailable, and the team's daily playing-time- unavailable metric — what fraction of recent starts come from currently-injured players — sits at 19.2 per cent. That is the highest in the league. New York has more of its production currently behind the curtain than any other team in baseball.
The story being told publicly this week is about Francisco Lindor's injured-list placement on 23 April and the cruel domino that followed when his replacement, Ronny Mauricio, broke his thumb sliding into first base on Friday. It is a clean narrative — superstar shortstop down, infield depth shredded, Bo Bichette forced back to a position he had been moved off of just months earlier. But the warehouse data tells a quieter and more complete version: the Mets' problems began on 4 April, when Juan Soto went on the injured list, and they have not got better since.
The injury timeline is itself the article. Soto was out from 4 April to 21 April — that is the team's seven-hundred-million-dollar off-season investment, missing for fifteen of the team's first twenty-two games. Jared Young (first base) went on the injured list on 13 April and has not returned, missing seventeen games to date. Jorge Polanco, the team's other starting first baseman, joined him on 15 April — fifteen games and counting. By the time Lindor went down on 23 April, the Mets were already running out half a starting infield. Lindor was the fourth domino, not the first.
Was Lindor the inflection point?
The headline statistical question from this stretch is: did Lindor's injury actually break what was working before, or were the Mets already broken? The data has a clear answer to half of it, with the appropriate caveat about sample size on the other half.
In the twenty-four games before Lindor's injury, the Mets went 8-16. Their Bayesian posterior 'true' win-rate estimate, with a weak Beta prior centred on .500, lands at 38.2 per cent with an 80 per cent credible interval of 27.8 to 49.0 per cent. The probability that their underlying ability in that pre-Lindor window was below .500 is roughly 95 per cent. They were already a bad team, and the data is confident about that.
In the nine games since Lindor went down, they are 3-6. The posterior estimate of their post-Lindor true win rate is 42.1 per cent — slightly higher than pre-Lindor, but the credible interval is so wide ([27.9 per cent, 56.7 per cent]) that the comparison is statistically inert. The probability that their post-Lindor underlying ability is genuinely lower than their pre-Lindor underlying ability is only 39 per cent — essentially a coin flip. With nine games of post-Lindor evidence, we cannot say whether the Lindor injury has made them worse, has made them better through some lineup-shake-up bounce, or has simply continued the existing pattern.
The honest reading is the third one. The team's last-30-day rolling form chart is essentially a flat line of mediocrity, beginning sometime in mid-April and extending unbroken to today. The injury-burden curve climbed steadily through the same window as more players joined the IL. There is no sharp inflection at 23 April. There is no Lindor-shaped drop in the win rate. There is just a slow, persistent loss of production from a roster that is, in aggregate, missing more talent than any other team in the league.
The shape of the problem
The injury burden is genuine, but it does not, on its own, explain the season the Mets are having. Two stat-tests make that case decisively.
The first is the Pythagorean record. Run differential is the noise-reducing version of win-loss — the win percentage you would expect a team to have given how many runs they scored and gave up. Through 33 games, the Mets have scored 113 runs and allowed 148. That gives them a Pythagorean win percentage of .368. Their actual record is .333 (11-22), with a Bayesian posterior estimate of .372 and an 80 per cent credible interval of [.280, .467]. The Pythagorean estimate sits squarely inside that interval. The Mets are not getting unlucky in close games, they are not blowing leads, and they are not on the wrong end of one-run swings. They are losing because they are not scoring.
The second test sharpens the point. The Mets' runs-scored-per-game is 3.42. The league average is 4.51. With a Bayesian gamma posterior on the team's true scoring rate, the 80 per cent credible interval is [3.04, 3.87]. The probability that the Mets' true offensive rate is at or above the league average is 0.13 per cent. By contrast, their runs-allowed-per-game is 4.48, with a credible interval of [4.05, 5.00] — a 50 per cent posterior probability of being worse than league average, which is to say, statistically indistinguishable from average. The pitching is fine. The defence is fine. The offence is, by a strict statistical reading, the most clearly broken thing on the roster.
A team that needs diagnosis, not just healing
The diagnosis the data points at, then, is that the Lindor injury — taken on its own — has not measurably moved the team's win expectation. The post-Lindor sample is too small to support the claim either way, and the team was already running a sub-.400 posterior win rate before he was hurt. The deeper problem is offensive, the data is unambiguous about it, and even the partial recovery of the lineup with Soto's return has not pulled the team out of it. The injury cascade is real. It is also a convenient frame for a problem that started before any of the injured-list placements and has not relented since the most prominent of those replacements walked back into the dugout.
What needs to change is therefore more uncomfortable than "Polanco and Young come back". It is, on the data: Bo Bichette starts hitting like he was paid to hit, the supporting cast around the two stars finds its 4.5-runs-per-game level rather than 3.4, and Carlos Mendoza either finds the lineup that works or stops being the manager. The Mets, by these numbers, are a team that needs to be diagnosed, not merely healed. Lindor coming back will help. It will not fix this on its own.
Methodology: IL stints from mlb_silver.stg_roster_history filtered to MLB-relevant codes (D7/D10/D15/D60/ILF/SU/BRV) for players appearing in 2026 NYM lineups. Bayesian win-rate posteriors use Beta(5+w, 5+l) priors. Run-rate tests use Gamma posteriors with conjugate priors. Pythagorean expectation uses RS²/(RS²+RA²). All data sourced from the warehouse repo's gold + silver layers. Analysis script and cached data are in the analysis/2026-05-nym-injuries folder.