Seattle Mariners 2026

Stats, refreshed the morning after every game

Published

June 23, 2026

Last refreshed: June 22, 2026 at 9:40 PM PDT

This site treats a baseball season like a bioinformatics experiment: players are samples, their offensive stats are features, and we look for the hitters and pitchers behaving differently from the rest. The numbers come from FanGraphs and the MLB Stats API and are re-rendered automatically the morning after each Mariners game.

Read the full differential analysis →

Who’s hot, who’s not

Hard contact vs results. Does hitting the ball hard translate into production? The dashed line is the league-wide trend; the grey band is its 95% confidence interval (how sure we are of that average relationship — not a per-player normal range). Players well above the line are out-producing their contact quality (lucky or crafty); well below, under-producing (unlucky or poor launch angles).

Code

chart_hard_hit(batting)

BABIP — luck on balls in play. League average is ~.300; sustained extremes are rare, so red bars tend to cool off and navy bars tend to warm up.

Code

chart_babip(batting)

Pitching: ERA vs the peripherals. Negative bars mean a pitcher has allowed fewer runs than his strikeouts, walks, and contact quality would predict.

Code

chart_pitching_luck(pitching)

The numbers

The biggest gaps between what hitters have actually produced (wOBA) and what their batted-ball quality says they should have produced (xwOBA).

Code

glance <- batting |>
  dplyr::transmute(
    Player = player,
    Pos    = pos,
    PA     = pa,
    wOBA   = round(woba, 3),
    xwOBA  = round(xwoba, 3),
    Diff   = round(woba - xwoba, 3)
  ) |>
  dplyr::arrange(dplyr::desc(Diff))

over  <- dplyr::slice_max(glance, Diff, n = 5, with_ties = FALSE)
under <- dplyr::slice_min(glance, Diff, n = 5, with_ties = FALSE) |>
  dplyr::arrange(Diff)

Code

knitr::kable(over, row.names = FALSE)
knitr::kable(under, row.names = FALSE)

Running hot — over-performing

Player	Pos	PA	wOBA	xwOBA	Diff
Colt Emerson	3B	101	0.334	0.228	0.105
Brendan Donovan	UNK	101	0.372	0.345	0.027
Randy Arozarena	UNK	302	0.365	0.353	0.012
Dominic Canzone	DH	201	0.390	0.384	0.006
Mitch Garver	C	112	0.287	0.295	-0.007

Running cold — under-performing

Player	Pos	PA	wOBA	xwOBA	Diff
Patrick Wisdom	UNK	44	0.172	0.265	-0.093
Connor Joe	RF	45	0.274	0.337	-0.063
Rob Refsnyder	DH	115	0.202	0.257	-0.055
Cal Raleigh	C	205	0.255	0.293	-0.038
Jhonny Pereda	UNK	68	0.309	0.346	-0.037

Pitching staff — ERA − xERA

Code

pitching |>
  dplyr::transmute(
    Player = player,
    Role   = role,
    IP     = ip,
    ERA    = round(era, 2),
    xERA   = round(xera, 2),
    `ERA − xERA` = round(era_minus_xera, 2)
  ) |>
  dplyr::arrange(`ERA − xERA`) |>
  knitr::kable(row.names = FALSE)

Player	Role	IP	ERA	xERA	ERA − xERA
Nick Davila	RP	12.2	0.00	3.99	-3.99
Matt Brash	RP	16.2	0.54	4.11	-3.57
Cole Wilcox	RP	13.1	5.40	7.50	-2.10
Eduard Bazardo	SP	34.1	2.10	4.12	-2.02
Gabe Speier	RP	18.2	1.93	3.80	-1.87
Emerson Hancock	SP	85.0	3.60	4.56	-0.96
Logan Gilbert	SP	93.0	3.29	3.93	-0.64
Bryce Miller	SP	40.0	1.57	2.15	-0.58
José A. Ferrer	SP	32.0	2.81	2.94	-0.13
Cooper Criswell	SP	30.2	3.52	3.31	0.21
George Kirby	SP	90.0	4.10	3.63	0.47
Luis Castillo	SP	70.2	5.22	4.61	0.61
Bryan Woo	SP	89.0	3.94	3.27	0.67
Andrés Muñoz	SP	27.1	5.27	4.36	0.91
Alex Hoppe	SP	22.0	5.32	3.56	1.76

How this updates: a scheduled GitHub Actions workflow checks each morning whether the Mariners finished a game the night before. If so, it re-fetches the stats, re-renders this site, and deploys it to Vercel. Off-days are skipped, and the synthetic fallback is never published.

--- title: "Seattle Mariners 2026" subtitle: "Stats, refreshed the morning after every game" date: today page-layout: full code-fold: true --- ```{r setup, include=FALSE} source(here::here("R", "00_setup.R")) source(here::here("R", "charts.R")) # In CI the data is fetched once before the render. When this page is rendered # standalone the .rds files may not exist yet — fetch then. if (!file.exists(here("data", "batting_2026.rds"))) { source(here::here("R", "01_fetch_data.R")) } batting <- readRDS(here("data", "batting_2026.rds")) pitching <- readRDS(here("data", "pitching_2026.rds")) refreshed <- format(Sys.time(), tz = "America/Los_Angeles", format = "%B %e, %Y at %l:%M %p %Z") # Note when the underlying numbers came from the synthetic fallback rather than # the live feed, so the page is never silently wrong. src <- tryCatch(readLines(here("data", "data_source.txt"), n = 1), error = function(e) "unknown") ``` ::: {.refreshed-badge} Last refreshed: `r refreshed` ::: ```{r live-warning, echo=FALSE, results='asis'} if (!identical(src, "live")) { cat("> ⚠️ **Heads up:** these numbers are from the synthetic fallback — the live", "FanGraphs / MLB Stats API feed was unavailable at render time.\n") } ``` This site treats a baseball season like a bioinformatics experiment: players are *samples*, their offensive stats are *features*, and we look for the hitters and pitchers behaving differently from the rest. The numbers come from FanGraphs and the MLB Stats API and are re-rendered automatically the morning after each Mariners game. [**Read the full differential analysis →**](/analysis) ## Who's hot, who's not **Hard contact vs results.** Does hitting the ball hard translate into production? The dashed line is the league-wide trend; the grey band is its 95% confidence interval (how sure we are of that *average* relationship — not a per-player normal range). Players well *above* the line are out-producing their contact quality (lucky or crafty); well *below*, under-producing (unlucky or poor launch angles). ```{r hard-hit, fig.width = 9, fig.height = 6} chart_hard_hit(batting) ``` **BABIP — luck on balls in play.** League average is ~.300; sustained extremes are rare, so red bars tend to cool off and navy bars tend to warm up. ```{r babip, fig.width = 9, fig.height = 5} chart_babip(batting) ``` **Pitching: ERA vs the peripherals.** Negative bars mean a pitcher has allowed fewer runs than his strikeouts, walks, and contact quality would predict. ```{r pitching-luck, fig.width = 9, fig.height = 5} chart_pitching_luck(pitching) ``` ## The numbers The biggest gaps between what hitters have actually produced (**wOBA**) and what their batted-ball quality says they *should* have produced (**xwOBA**). ```{r glance} glance <- batting |> dplyr::transmute( Player = player, Pos = pos, PA = pa, wOBA = round(woba, 3), xwOBA = round(xwoba, 3), Diff = round(woba - xwoba, 3) ) |> dplyr::arrange(dplyr::desc(Diff)) over <- dplyr::slice_max(glance, Diff, n = 5, with_ties = FALSE) under <- dplyr::slice_min(glance, Diff, n = 5, with_ties = FALSE) |> dplyr::arrange(Diff) ``` ::: {layout-ncol="2"} ::: {} **Running hot — over-performing** ```{r over} knitr::kable(over, row.names = FALSE) ``` ::: ::: {} **Running cold — under-performing** ```{r under} knitr::kable(under, row.names = FALSE) ``` ::: ::: **Pitching staff — ERA − xERA** ```{r pitching-glance} pitching |> dplyr::transmute( Player = player, Role = role, IP = ip, ERA = round(era, 2), xERA = round(xera, 2), `ERA − xERA` = round(era_minus_xera, 2) ) |> dplyr::arrange(`ERA − xERA`) |> knitr::kable(row.names = FALSE) ``` --- *How this updates: a scheduled GitHub Actions workflow checks each morning whether the Mariners finished a game the night before. If so, it re-fetches the stats, re-renders this site, and deploys it to Vercel. Off-days are skipped, and the synthetic fallback is never published.*