Andrew's Musings

MLB: What to watch on August 16, 2025

Here are today's MLB games, ordered by watchability, based on how interesting the teams and starting pitchers look. Higher is better. For more information, read this post.

Notes:

Detail

Detroit Tigers @ Minnesota Twins, 4:15p

Summary

This is the sort of game that reads like a scouting report: an electric young arm (Zebby Matthews) vs. a healthy, refined hurler working to prove his redemption (Casey Mize), and the result should be a tense, pitch-centric watch — with fireworks possible from Detroit’s offense. Zebby is the real storyline: his gaudy pNERD comes from a sub-70 xFIP-, jumpy whiff/strike profile, and plus velocity that’s climbed into the high-90s after a dominant Triple‑A run — the Twins have been promoting him and riding that fastball-heavy profile into the rotation. Mize’s start matters because he’s back from a hamstring IL stint and has shown better velocity and command post‑TJ; he’s more of a control/strike‑rate pitcher here, which sets up a contrast in approach. Team context pushes watchability higher: Detroit’s tNERD is driven by barrel rate, baserunning, and youth, while Minnesota’s bullpen and Matthews’ profile add volatility. With a gNERD of 14.21 — the top of today’s slate and near historical elite levels — prioritize this one if you like watching power stuff tested by a savvy, improving lineup.

(A model from OpenAI generated this text using instructions, the NERD scores, and these sources: 1, 2, 3, 4.)

Detroit Tigers

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat 24.8 9.7% 4.8 10.4 5.6 $148.2M 27.6 -25.0
Z-score 0.49 0.94 0.76 0.54 -1.13 -0.33 -1.14 -1.19
tNERD 0.49 0.94 0.76 0.54 -1.13 0.33 1.14 0.00 4.00 7.08

Minnesota Twins

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat -20.3 8.8% -7.6 -10.2 48.1 $145.1M 28.8 11.0
Z-score -0.36 0.20 -1.33 -0.53 1.31 -0.37 0.08 0.51
tNERD -0.36 0.20 -1.33 -0.53 1.31 0.37 0.00 0.51 4.00 4.19

Casey Mize, Detroit Tigers

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 98 10.4% 66.0% 94.7 mph 28 17.8s -12 0.0%
Z-score -0.17 -0.06 0.82 0.39 -0.19 -0.59
pNERD 0.35 -0.03 0.41 0.39 0.19 0.30 0.00 0.00 3.80 5.40

Zebby Matthews, Minnesota Twins

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 70 13.0% 66.8% 96.6 mph 25 16.5s 56 0.0%
Z-score -1.85 1.20 1.13 1.25 -0.96 -1.64
pNERD 3.70 0.60 0.57 1.25 0.96 0.82 0.05 0.00 3.80 11.74

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Seattle Mariners @ New York Mets, 1:10p

Summary

Good pitching meets debut drama: Bryan Woo’s steady, high-spin heat vs. a Mets prospect making his big-league debut turns what could be a routine afternoon into one you should at least tune in for. The gNERD (13.58) sits well above the historic 75th percentile and near the top of today’s slate, driven by a Mets lineup that profiles as more eventful (tNERD 8.98) than Seattle’s (5.32) — New York’s barrel rate, baserunning and a leaky bullpen point to late-game volatility. Bryan Woo’s strong pNERD (7.86) mirrors his season of length and punch (regularly going six-plus with big strikeout outings and mid-90s velocity), although his deliberate pace trims some immediacy — he’s the reliable engine here. Nolan McLean’s pNERD of 0 simply flags no MLB track record, but he’s a top prospect with crisp Triple-A results and a nasty sweeper — the debut narrative itself raises watchability. Toss in the Mets’ recent struggles and the roster shake-up that summoned McLean, and you get meaningful stakes plus the tidy contrast of established arm vs. hopeful newcomer.

(A model from OpenAI generated this text using instructions, the NERD scores, and these sources: 1, 2, 3, 4, 5.)

Seattle Mariners

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat 57.7 9.3% -1.5 -16.5 14.5 $152.8M 28.2 12.0
Z-score 1.11 0.61 -0.30 -0.85 -0.62 -0.27 -0.53 0.56
tNERD 1.11 0.61 -0.30 -0.85 -0.62 0.27 0.53 0.56 4.00 5.32

New York Mets

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat 41.1 10.3% 6.2 3.6 37.9 $332.0M 29.7 18.0
Z-score 0.79 1.43 1.00 0.19 0.73 2.14 1.00 0.84
tNERD 0.79 1.43 1.00 0.19 0.73 0.00 0.00 0.84 4.00 8.98

Bryan Woo, Seattle Mariners

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 85 12.2% 67.9% 95.5 mph 25 20.4s -4 0.0%
Z-score -0.95 0.81 1.59 0.75 -0.96 1.50
pNERD 1.90 0.41 0.80 0.75 0.96 -0.75 0.00 0.00 3.80 7.86

Nolan McLean, New York Mets

No detailed stats available

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San Diego Padres @ Los Angeles Dodgers, 6:10p

Summary

Good bet if you like high-spin, high-velocity strikeout pitchers versus a heavy-hitting lineup; less compelling if you want a predictable pitcher duel. Dylan Cease is the real attraction here—his pNERD (8.38) reflects above-average swing‑and‑miss and elite velocity, and he’s been eating innings again in recent starts, so expect strikeouts and chase pitches. The Dodgers’ team profile (tNERD 7.21) boosts watchability: league-leading barrel rate and batting runs mean Cease will be tested in every count, turning his whiffs into high-stakes action. The opposing starter’s pNERD shows 0.00 (our dataset had no pitcher stats for Blake Snell), but he’s just come off a long IL stint and returned with a swing‑and‑miss heavy outing and double-digit strikeouts in a recent turn, which adds storyline uncertainty and upside. With a gNERD near the upper quartile (12.96), this is a must‑watch for fans who prefer volatile, batter‑vs‑pitcher fireworks rather than low‑scoring chess.

(A model from OpenAI generated this text using instructions, the NERD scores, and these sources: 1, 2, 3, 4.)

San Diego Padres

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat 5.5 7.0% -1.0 -1.9 63.3 $209.3M 30.0 13.0
Z-score 0.12 -1.27 -0.22 -0.10 2.19 0.49 1.30 0.61
tNERD 0.12 -1.27 -0.22 -0.10 2.19 0.00 0.00 0.61 4.00 5.33

Los Angeles Dodgers

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat 79.7 9.8% 0.2 -7.8 44.0 $341.0M 29.6 -2.0
Z-score 1.52 1.02 -0.01 -0.40 1.08 2.26 0.89 -0.10
tNERD 1.52 1.02 -0.01 -0.40 1.08 0.00 0.00 0.00 4.00 7.21

Dylan Cease, San Diego Padres

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 81 15.9% 63.5% 97.1 mph 29 19.8s 31 0.0%
Z-score -1.19 2.61 -0.24 1.47 0.07 1.02
pNERD 2.38 1.30 -0.12 1.47 0.00 -0.51 0.05 0.00 3.80 8.38

Blake Snell, Los Angeles Dodgers

No detailed stats available

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New York Yankees @ St. Louis Cardinals, 4:15p

Summary

Worth a look if you like low-variance pitching with a pop of offense: Sonny Gray brings the swing-and-miss profile, while Max Fried is a steady lefty whose peripherals suggest a game that can stay tight until the bullpens decide otherwise. The gNERD (12.88) sits comfortably above both today’s mean and the historic median, driven by a strong Yankees tNERD (7.74) — their barrel rate and batting runs promise the power to break a pitchers’ duel — and a high pNERD for Gray (7.51) thanks to an excellent xFIP- and strikeout profile. Sonny Gray also looks to be trending well coming off a seven-inning, one-run outing and has an established K track record that underpins that pNERD. Max Fried’s numbers are tidy but a touch less intimidating (pNERD 5.06; xFIP- higher), and his last outing was bumpier, which makes him a matchup the Cardinals can exploit if they square him up. St. Louis’s elite defense and competent bullpen raise the odds of a low-scoring, strategic game that rewards patience — entertaining if you favor pitching craft and a late-inning bullpen subplot.

(A model from OpenAI generated this text using instructions, the NERD scores, and these sources: 1, 2, 3, 4.)

New York Yankees

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat 80.2 11.4% -2.7 6.6 16.8 $290.9M 29.1 11.0
Z-score 1.53 2.34 -0.50 0.35 -0.48 1.58 0.38 0.51
tNERD 1.53 2.34 -0.50 0.35 -0.48 0.00 0.00 0.51 4.00 7.74

St. Louis Cardinals

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat -3.9 7.8% -3.8 26.4 39.2 $135.7M 28.6 -4.0
Z-score -0.06 -0.61 -0.69 1.37 0.80 -0.50 -0.13 -0.20
tNERD -0.06 -0.61 -0.69 1.37 0.80 0.50 0.13 0.00 4.00 5.45

Max Fried, New York Yankees

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 84 10.8% 63.3% 94.1 mph 31 20.5s -11 0.0%
Z-score -1.01 0.13 -0.31 0.12 0.58 1.58
pNERD 2.02 0.07 -0.16 0.12 0.00 -0.79 0.00 0.00 3.80 5.06

Sonny Gray, St. Louis Cardinals

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 72 11.9% 66.8% 92.0 mph 35 20.3s 27 0.0%
Z-score -1.73 0.67 1.16 -0.83 1.61 1.42
pNERD 3.46 0.33 0.58 0.00 0.00 -0.71 0.05 0.00 3.80 7.51

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Milwaukee Brewers @ Cincinnati Reds, 3:40p

Summary

If you want action, this one earns a polite nod: a gNERD of 11.86 leans toward watchable because Milwaukee’s team profile and Priester’s above-average pNERD promise eventful innings even if Cincinnati’s team NERD is tepid. The Brewers’ high tNERD is driven by elite baserunning and glovework (those components jump off the sheet), so expect the kind of scrappy, run-created moments that break deadlocks; Cincinnati’s lower tNERD suggests fewer built-in fireworks from the lineup. Quinn Priester (pNERD 6.31) is the bigger pitching draw—young, relatively lively in profile and pitching with pace—yet he’s coming off a rough outing that makes his next start both a bounce-back opportunity and a little unpredictable. Zack Littell (pNERD 4.54) is more of a steady, contact-oriented arm with decent strike percentages but limited swing-and-miss upside, which means the game could tilt into hitter-friendly sequences if the Brewers barrel him. The broader storyline — Milwaukee’s lengthy win streak and roster moves around recent returns — adds human drama, so tune in if you like momentum-and-revenge subplots paired with pitchers who can both implode and sparkle.

(A model from OpenAI generated this text using instructions, the NERD scores, and these sources: 1, 2, 3, 4.)

Milwaukee Brewers

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat 50.2 6.4% 14.9 25.1 34.0 $112.2M 27.6 -44.0
Z-score 0.96 -1.76 2.46 1.31 0.50 -0.82 -1.14 -2.09
tNERD 0.96 -1.76 2.46 1.31 0.50 0.82 1.14 0.00 4.00 9.43

Cincinnati Reds

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat -34.1 7.0% 5.6 -1.2 20.1 $115.7M 28.7 -29.0
Z-score -0.62 -1.27 0.89 -0.06 -0.29 -0.77 -0.02 -1.38
tNERD -0.62 -1.27 0.89 -0.06 -0.29 0.77 0.02 0.00 4.00 3.44

Quinn Priester, Milwaukee Brewers

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 93 9.8% 63.0% 93.8 mph 24 16.7s -9 0.0%
Z-score -0.47 -0.35 -0.44 -0.02 -1.22 -1.48
pNERD 0.95 -0.18 -0.22 0.00 1.22 0.74 0.00 0.00 3.80 6.31

Zack Littell, Cincinnati Reds

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 101 9.5% 67.8% 91.9 mph 29 18.0s -13 0.0%
Z-score 0.00 -0.50 1.57 -0.88 0.07 -0.43
pNERD -0.01 -0.25 0.78 0.00 0.00 0.22 0.00 0.00 3.80 4.54

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Pittsburgh Pirates @ Chicago Cubs, 11:20a

Summary

This is a watchable mismatch because Chicago’s team gear (tNERD 8.34) promises action while Pittsburgh’s offense (tNERD 3.02) drags the overall game toward low-scoring unpredictability — but Mike Burrows (pNERD 6.85) gives it swing-and-miss intrigue against Shōta Imanaga’s more methodical profile (pNERD 3.97). The Cubs’ high tNERD comes from balanced offense, barrels and above-average fielding that should manufacture chances, while the Pirates’ low team marks stem from a thin batting profile; that split alone elevates the game above an average day (gNERD 11.09 is a touch cleaner than today’s mean). Burrows has shown the ability to miss bats and carry lively velo (95+ mph) but also gave up four runs and six Ks in a five-inning turn on August 10, so expect high-leverage platoon swings when he’s off. Imanaga’s recent no-walk, high-K outings suggest excellent command and low volatility, which suppresses scoring but makes any Cubs breaks feel earned. Injuries are notable but not game-breaking for lineup structure; check final scratches before first pitch.

(A model from OpenAI generated this text using instructions, the NERD scores, and these sources: 1, 2, 3, 4, 5.)

Pittsburgh Pirates

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat -103.5 8.0% -7.4 10.8 33.7 $88.9M 28.4 4.0
Z-score -1.93 -0.45 -1.29 0.56 0.49 -1.13 -0.33 0.18
tNERD -1.93 -0.45 -1.29 0.56 0.49 1.13 0.33 0.18 4.00 3.02

Chicago Cubs

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat 61.3 9.9% 8.4 25.7 14.0 $197.7M 30.6 -14.0
Z-score 1.17 1.11 1.37 1.34 -0.64 0.33 1.91 -0.67
tNERD 1.17 1.11 1.37 1.34 -0.64 0.00 0.00 0.00 4.00 8.34

Mike Burrows, Pittsburgh Pirates

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 96 11.2% 63.9% 95.4 mph 25 17.0s 13 0.0%
Z-score -0.29 0.33 -0.06 0.70 -0.96 -1.24
pNERD 0.59 0.16 -0.03 0.70 0.96 0.62 0.05 0.00 3.80 6.85

Shota Imanaga, Chicago Cubs

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 108 11.7% 68.2% 90.9 mph 31 18.9s -31 0.0%
Z-score 0.42 0.57 1.76 -1.33 0.58 0.29
pNERD -0.85 0.28 0.88 0.00 0.00 -0.15 0.00 0.00 3.80 3.97

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Miami Marlins @ Boston Red Sox, 1:10p

Summary

This isn't a high-octane pitching showcase so much as a modestly watchable Fenway tilt: Boston's team strengths and lineup juice make the game more interesting than the pitchers' low swing‑and‑miss profiles would suggest. With a gNERD of 10.05—basically average for the day—the matchup leans on the Red Sox's strong team profile (high barrel, baserunning and fielding contributions) versus a Marlins club that has depth questions and a shaky rotation. Cal Quantrill is a veteran innings eater signed to steady Miami's staff, but his pNERD and underlying peripherals show limited swing‑and‑miss and below‑average strike tendencies; expect contact and length rather than punchouts. Brayan Bello grades a touch better on pNERD thanks to higher velocity and youth, and he’s already worked back from early shoulder trouble to make season starts, so he’s the more intriguing arm but not a true miss‑machine. If you prefer offense, Fenway’s advantages and Boston’s team metrics make this worth a glance; if you want premium pitching fireworks, this one likely won’t deliver.

(A model from OpenAI generated this text using instructions, the NERD scores, and these sources: 1, 2, 3.)

Miami Marlins

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat -17.6 8.0% -2.0 5.3 16.1 $67.3M 26.8 6.0
Z-score -0.31 -0.45 -0.38 0.28 -0.52 -1.42 -1.96 0.28
tNERD -0.31 -0.45 -0.38 0.28 -0.52 1.42 1.96 0.28 4.00 6.26

Boston Red Sox

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat 24.5 9.8% 5.0 18.8 45.4 $191.8M 28.7 -13.0
Z-score 0.48 1.02 0.79 0.98 1.16 0.25 -0.02 -0.62
tNERD 0.48 1.02 0.79 0.98 1.16 0.00 0.02 0.00 4.00 8.46

Cal Quantrill, Miami Marlins

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 111 8.7% 62.5% 93.7 mph 30 18.8s 8 0.0%
Z-score 0.60 -0.89 -0.64 -0.06 0.32 0.21
pNERD -1.20 -0.44 -0.32 0.00 0.00 -0.11 0.05 0.00 3.80 1.78

Brayan Bello, Boston Red Sox

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 102 8.7% 62.0% 95.2 mph 26 19.8s -25 0.0%
Z-score 0.06 -0.89 -0.87 0.61 -0.70 1.02
pNERD -0.13 -0.44 -0.44 0.61 0.70 -0.51 0.00 0.00 3.80 3.60

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Texas Rangers @ Toronto Blue Jays, 12:07p

Summary

Middling, but with a clear flavor: a veteran bounce-back on the mound versus a contact-heavy Blue Jays offense and a Rangers defense that can make a low-strikeout outing feel tidy. Corbin’s storyline — a late-career revival after grinding through bad years, pitching better than many expected — gives the game narrative interest, but his underlying peripherals are merely league-average (hence a modest pNERD). Eric Lauer profiles as the slightly steadier option by surface metrics (better xFIP-ish marks and strike percentage), so the matchup leans toward pitcher control rather than fireworks. The Blue Jays supply real offensive heft and contact skills, which raises watchability because they’ll force both pitchers to throw strikes and work for outs; their team batting and on-base results are among MLB’s best. Countering that, the Rangers’ defense is legitimately excellent and can suppress what looks like an otherwise ordinary pitcher duel, lowering explosive scoring but making for crisp, efficient baseball. All told, the gNERD sits near average: appealing if you like matchup chess and stories (Corbin’s comeback, Lauer’s steadiness), less so if you want high-octane offense.

(A model from OpenAI generated this text using instructions, the NERD scores, and these sources: 1, 2, 3, 4, 5.)

Texas Rangers

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat -60.4 8.7% 8.6 17.6 33.1 $219.7M 30.4 -30.0
Z-score -1.12 0.12 1.40 0.92 0.45 0.63 1.71 -1.43
tNERD -1.12 0.12 1.40 0.92 0.45 0.00 0.00 0.00 4.00 5.77

Toronto Blue Jays

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat 74.0 8.2% -5.6 24.7 27.3 $248.4M 29.6 27.0
Z-score 1.41 -0.29 -0.99 1.29 0.12 1.01 0.89 1.27
tNERD 1.41 -0.29 -0.99 1.29 0.12 0.00 0.00 1.27 4.00 6.81

Patrick Corbin, Texas Rangers

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 101 10.9% 62.7% 91.4 mph 35 18.4s -1 0.0%
Z-score 0.00 0.18 -0.60 -1.10 1.61 -0.11
pNERD -0.01 0.09 -0.30 0.00 0.00 0.05 0.00 0.00 3.80 3.64

Eric Lauer, Toronto Blue Jays

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 97 9.3% 66.5% 91.7 mph 30 20.2s -27 0.0%
Z-score -0.23 -0.59 1.04 -0.97 0.32 1.34
pNERD 0.47 -0.30 0.52 0.00 0.00 -0.67 0.00 0.00 3.80 3.82

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Atlanta Braves @ Cleveland Guardians, 4:10p

Summary

Not a must-watch on the marquee — gNERD 8.98 sits below today’s average and around the lower half of typical games — but there are enough microplots to make it worth a nibble if you like pitcher storylines. Slade Cecconi (pNERD 5.20) is the main attraction: he’s shown the ability to pound the zone and rack up whiffs in his early Guardians outings, including an eight‑K debut that suggested a legit swing-and-miss profile for Cleveland’s rotation.

Joey Wentz (pNERD 1.76) is the counterpoint — a low‑strikeout, modest‑velocity arm whose recent hot streak for Atlanta looks encouraging in box scores but sits on a shaky underlying xFIP and thin track record, so expect contact and a fair number of balls in play rather than dominant frames.

Team-wise the game is middling: both tNERDs are near league average, Cleveland’s defense and bullpen add intrigue while their offense is one of the weaker pieces, and Atlanta’s fielding and an unusually large “luck” component hint the Braves could outperform their peripherals. Overall, watch if you want to see Cecconi test his profile against a contact‑oriented Braves staff; don’t expect a high‑octane showdown.

(A model from OpenAI generated this text using instructions, the NERD scores, and these sources: 1, 2, 3, 4.)

Atlanta Braves

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat -5.9 9.2% -3.1 13.5 16.9 $216.2M 29.4 20.0
Z-score -0.09 0.53 -0.57 0.70 -0.48 0.58 0.69 0.94
tNERD -0.09 0.53 -0.57 0.70 -0.48 0.00 0.00 0.94 4.00 5.04

Cleveland Guardians

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat -60.0 6.7% 4.3 15.0 41.5 $102.3M 27.5 -25.0
Z-score -1.11 -1.52 0.68 0.78 0.93 -0.95 -1.25 -1.19
tNERD -1.11 -1.52 0.68 0.78 0.93 0.95 1.25 0.00 4.00 5.96

Joey Wentz, Atlanta Braves

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 115 10.7% 63.8% 93.8 mph 27 20.6s 5 0.0%
Z-score 0.84 0.08 -0.13 -0.02 -0.45 1.66
pNERD -1.68 0.04 -0.07 0.00 0.45 -0.83 0.05 0.00 3.80 1.76

Slade Cecconi, Cleveland Guardians

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 97 10.0% 64.9% 94.1 mph 26 18.5s 5 0.0%
Z-score -0.23 -0.26 0.35 0.12 -0.70 -0.03
pNERD 0.47 -0.13 0.18 0.12 0.70 0.01 0.05 0.00 3.80 5.20

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Los Angeles Angels @ Athletics, 7:05p

Summary

Not exactly must-see TV, but this one trades high drama for a cheap thrill: a 22-year-old A’s prospect getting a look in the rotation against a veteran Angel whose stuff has faded. The gNERD of 7.39 sits below both today’s average and the historical mean, so don’t expect a classic, but Oakland’s team profile — driven by offensive glue and an unusually large “luck” bump — lifts the game’s intrigue. Tyler Anderson’s peripherals and diminished velocity suggest a platoon-vulnerable veteran start rather than dominant innings, so contact and length look iffy. Luis Morales is the storyline: a highly regarded 2025 prospect who dominated at Triple‑A and has only the briefest MLB notebook (two innings), so his start offers novelty and scouting value even if the sample is tiny. Between Oakland’s higher tNERD and the Angels’ defensive and bullpen weaknesses, this is more of a scouting/hopeful-watch than a must-watch pitching duel; tune in if you like rookie debuts and offense-leaning skirmishes rather than textbook pitching.

(A model from OpenAI generated this text using instructions, the NERD scores, and these sources: 1, 2, 3, 4, 5.)

Los Angeles Angels

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat -17.1 10.7% -1.7 -41.2 -5.9 $203.8M 29.2 -14.0
Z-score -0.30 1.76 -0.33 -2.14 -1.79 0.41 0.49 -0.67
tNERD -0.30 1.76 -0.33 -2.14 -1.79 0.00 0.00 0.00 4.00 1.20

Athletics

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat 31.0 8.3% 0.0 -17.4 13.2 $77.1M 27.6 44.0
Z-score 0.60 -0.20 -0.05 -0.90 -0.69 -1.29 -1.14 2.07
tNERD 0.60 -0.20 -0.05 -0.90 -0.69 1.29 1.14 2.00 4.00 7.19

Tyler Anderson, Los Angeles Angels

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 125 11.5% 64.4% 89.2 mph 35 18.1s -12 0.0%
Z-score 1.44 0.47 0.12 -2.10 1.61 -0.35
pNERD -2.88 0.24 0.06 0.00 0.00 0.18 0.00 0.00 3.80 1.39

Luis Morales, Athletics

No detailed stats available

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Philadelphia Phillies @ Washington Nationals, 1:05p

Summary

This isn’t the can’t-miss duel you tell your friends about — gNERD 7.21 puts it below today’s average — but it’s watchable if you care about a veteran pitcher trying to steady a contender and a young arm making an emotional return. Taijuan Walker’s pNERD is modest (2.38) because his stuff hasn’t been elite this year — his xFIP- and chase/swing numbers suggest fewer swing-and-miss weapons, though he’s been efficient and produced two quality starts (1 run in 12 IP) recently. The Phillies’ tNERD (6.46) reflects real upside: above-average baserunning and power components that can manufacture runs against a Nationals roster that grades poorly on defense and bullpen runs (tNERD 0.59). The interesting subplot is Cade Cavalli: officially low-data in the pNERD here, but he’s back from Tommy John and flashed 100 mph life and a strong short outing that makes him a live hand early, even if durability/command remain questions. One caveat for viewers: Philly’s relief picture just took a scare with Jhoan Duran carted for evaluation, which could matter late.

(A model from OpenAI generated this text using instructions, the NERD scores, and these sources: 1, 2, 3, 4.)

Philadelphia Phillies

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat 22.0 8.9% 6.9 3.4 24.9 $279.5M 29.5 10.0
Z-score 0.43 0.29 1.11 0.18 -0.02 1.43 0.79 0.47
tNERD 0.43 0.29 1.11 0.18 -0.02 0.00 0.00 0.47 4.00 6.46

Washington Nationals

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat -30.8 7.8% -2.8 -38.1 -5.4 $115.9M 27.5 -18.0
Z-score -0.56 -0.61 -0.52 -1.98 -1.76 -0.77 -1.25 -0.86
tNERD -0.56 -0.61 -0.52 -1.98 -1.76 0.77 1.25 0.00 4.00 0.59

Taijuan Walker, Philadelphia Phillies

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 110 7.2% 62.8% 92.2 mph 32 16.7s -31 0.0%
Z-score 0.54 -1.61 -0.54 -0.74 0.84 -1.48
pNERD -1.09 -0.81 -0.27 0.00 0.00 0.74 0.00 0.00 3.80 2.38

Cade Cavalli, Washington Nationals

No detailed stats available

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Arizona Diamondbacks @ Colorado Rockies, 5:10p

Summary

Not a must-watch: the game’s gNERD (7.21) and the team NERD gap say there’s more mismatch than drama—Arizona’s offense is the main attraction while Colorado is mostly a cautionary tale. Ryne Nelson profiles as a competent, moderately interesting arm (pNERD 4.82)—decent velocity, usable strikeout profile, a few recent spotty outings—but nothing that guarantees fireworks; Chase Dollander is the real storyline only because he’s a 23‑year‑old prospect being handed another start, bringing high velocity and prospect intrigue despite shaky underlying results (his surface stats have been rough). The D‑backs’ tNERD (5.80) reflects a top‑half offense and some baserunning/defense value that can produce action, while Colorado’s negative tNERD (−0.27) tracks a historically bad lineup and bullpen that make sustained competitiveness unlikely; that combination depresses overall watchability. Expect a handful of batting highlights if Arizona’s launch departments show up and a few chase‑pitch swings when Dollander ramps heat, but don’t pencil this in as “can’t miss.”

(A model from OpenAI generated this text using instructions, the NERD scores, and these sources: 1, 2, 3, 4, 5.)

Arizona Diamondbacks

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat 60.2 9.2% 4.5 7.2 -3.2 $189.5M 29.5 14.0
Z-score 1.15 0.53 0.71 0.38 -1.63 0.22 0.79 0.65
tNERD 1.15 0.53 0.71 0.38 -1.63 0.00 0.00 0.65 4.00 5.80

Colorado Rockies

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat -116.1 8.2% -8.3 -28.6 -2.4 $125.9M 27.9 26.0
Z-score -2.17 -0.29 -1.44 -1.48 -1.59 -0.63 -0.84 1.22
tNERD -2.17 -0.29 -1.44 -1.48 -1.59 0.63 0.84 1.22 4.00 -0.27

Ryne Nelson, Arizona Diamondbacks

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 97 9.0% 65.3% 95.6 mph 27 20.0s -16 0.0%
Z-score -0.23 -0.74 0.54 0.79 -0.45 1.18
pNERD 0.47 -0.37 0.27 0.79 0.45 -0.59 0.00 0.00 3.80 4.82

Chase Dollander, Colorado Rockies

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 119 9.6% 61.0% 97.6 mph 23 18.3s 14 0.0%
Z-score 1.08 -0.45 -1.30 1.70 -1.47 -0.19
pNERD -2.16 -0.22 -0.65 1.70 1.47 0.09 0.05 0.00 3.80 4.08

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Baltimore Orioles @ Houston Astros, 4:10p

Summary

Not a can't-miss tilt — the gNERD (7.14) flags this as below-average entertainment — but it's quietly watchable if you care about small wrinkles: an unfamiliar Orioles starter and a newly rejuvenated Jason Alexander. The low gNERD is driven by a weak Orioles tNERD (1.90) and middling pitcher scores (avg pNERD 3.52), so expect limited offensive fireworks and a pitcher-centric game rather than a slugfest; the Astros' higher tNERD (5.34) mostly reflects bullpen strength and a positive “luck” component. Rico García's pNERD is 0.00, meaning there's effectively no starter profile to lean on — he's recently been a roster add/activation for Baltimore, so his role and true performance ceiling are uncertain. Jason Alexander's pNERD of 2.04 is below today's pitcher-average, but his recent string of scoreless, efficient outings since joining Houston suggests upside if he locates his changeup and keeps contact weak. Houston's injury list and roster churn make their lineup shakier but their bullpen and situational depth still tilt the game toward the home team. If you want low-key, strategic baseball with a few narrative threads (new O's arm; Alexander's mini-resurgence), tune in; otherwise, it ranks low on a day with flashier options.

(A model from OpenAI generated this text using instructions, the NERD scores, and these sources: 1, 2, 3, 4, 5.)

Baltimore Orioles

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat -16.1 9.1% -4.0 -17.6 12.9 $167.6M 29.2 -13.0
Z-score -0.29 0.45 -0.72 -0.91 -0.71 -0.07 0.49 -0.62
tNERD -0.29 0.45 -0.72 -0.91 -0.71 0.07 0.00 0.00 4.00 1.90

Houston Astros

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat 15.7 7.7% -4.0 2.4 43.5 $221.9M 29.0 27.0
Z-score 0.31 -0.70 -0.72 0.13 1.05 0.66 0.28 1.27
tNERD 0.31 -0.70 -0.72 0.13 1.05 0.00 0.00 1.27 4.00 5.34

Rico Garcia, Baltimore Orioles

No detailed stats available

Jason Alexander, Houston Astros

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 113 9.8% 60.8% 91.4 mph 32 17.3s 9 0.0%
Z-score 0.72 -0.35 -1.38 -1.10 0.84 -1.00
pNERD -1.44 -0.18 -0.69 0.00 0.00 0.50 0.05 0.00 3.80 2.04

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Tampa Bay Rays @ San Francisco Giants, 6:05p

Summary

Not a must-see: a gNERD of 7.03 sits below today's average and promises modest action more than fireworks. Still, Tampa Bay's sneaky baserunning and two live bullpens give this one a shot at being a quietly watchable outing rather than a total slog. The Rays carry the better tNERD (6.39) thanks to +8.9 baserunning runs and an active, above-average bullpen, while the Giants' tNERD (1.97) is dragged down by a stalled offense—San Francisco just placed Matt Chapman on the IL and shuffled the roster, which underlines how thin their lineup looks. Both starters are low-end pNERD types: Adrian Houser (3.30) profiles as a contact/groundball arm with middling xFIP and a recent scoreless outing after bouncing between teams, and Justin Verlander (2.41) is the veteran storyline—big name, declining peripherals (worse xFIP-) and innings that may limit swing-for-the-fences baseball. Expect a game decided by small margins: timely baserunning, bullpen matchup swings, or a defensive mistake rather than a two-strikeout pitching duel, so watch if you like late-inning chess more than strikeout theater.

(A model from OpenAI generated this text using instructions, the NERD scores, and these sources: 1, 2, 3, 4.)

Tampa Bay Rays

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat -2.5 7.6% 8.9 -29.1 39.0 $89.9M 27.4 -18.0
Z-score -0.03 -0.78 1.45 -1.51 0.79 -1.12 -1.35 -0.86
tNERD -0.03 -0.78 1.45 -1.51 0.79 1.12 1.35 0.00 4.00 6.39

San Francisco Giants

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat -29.0 7.4% -7.8 10.1 30.0 $195.3M 29.3 -8.0
Z-score -0.53 -0.94 -1.36 0.53 0.27 0.30 0.59 -0.39
tNERD -0.53 -0.94 -1.36 0.53 0.27 0.00 0.00 0.00 4.00 1.97

Adrian Houser, Tampa Bay Rays

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 106 8.7% 64.8% 94.4 mph 32 18.2s -36 0.0%
Z-score 0.30 -0.89 0.32 0.25 0.84 -0.27
pNERD -0.61 -0.44 0.16 0.25 0.00 0.13 0.00 0.00 3.80 3.30

Justin Verlander, San Francisco Giants

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 114 10.8% 64.9% 94.2 mph 42 19.1s -3 0.0%
Z-score 0.78 0.13 0.34 0.16 3.41 0.46
pNERD -1.56 0.07 0.17 0.16 0.00 -0.23 0.00 0.00 3.80 2.41

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Chicago White Sox @ Kansas City Royals, 4:10p

Summary

Low-voltage outing ahead: both teams’ offenses are anaemic and the starters bring little in the way of swing-and-miss or elite peripherals — the only real plot is Michael Lorenzen’s attempt to come back from an oblique layoff. Sean Burke and Lorenzen both register weak pNERDs, and the boards reflect that this is the least watchable game of the day. The White Sox’s tNERD is dragged down by awful batting runs and poor defense, so even if Burke eats innings his profile (high xFIP-, modest Ks) suggests bland contact-heavy outings rather than fireworks. Sean Burke’s season has been strikeout-heavy but uneven, which explains his low pNERD despite some upside. Michael Lorenzen gives the game a narrative: he was on the 15-day IL with a left oblique and has been doing rehab work and short outings as he readies for a return, which slightly lifts intrigue but doesn’t change the matchup’s underlying dullness. Kansas City’s positive luck signal hints they could outperform expectations briefly, but analytically this one ranks near the bottom for watchability.

(A model from OpenAI generated this text using instructions, the NERD scores, and these sources: 1, 2, 3.)

Chicago White Sox

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat -76.8 8.1% -4.0 -26.1 30.6 $79.0M 27.5 -9.0
Z-score -1.43 -0.37 -0.72 -1.35 0.31 -1.26 -1.25 -0.43
tNERD -1.43 -0.37 -0.72 -1.35 0.31 1.26 1.25 0.00 4.00 2.95

Kansas City Royals

Batting runs Barrel % Baserunning runs Fielding runs Bullpen runs Payroll Age Luck Constant Total
Raw stat -62.4 7.5% -2.6 7.3 28.1 $130.0M 28.8 28.0
Z-score -1.16 -0.86 -0.48 0.38 0.16 -0.58 0.08 1.32
tNERD -1.16 -0.86 -0.48 0.38 0.16 0.58 0.00 1.32 4.00 3.94

Sean Burke, Chicago White Sox

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 116 10.2% 61.7% 94.2 mph 25 18.7s -12 0.0%
Z-score 0.90 -0.16 -1.00 0.16 -0.96 0.13
pNERD -1.80 -0.08 -0.50 0.16 0.96 -0.07 0.00 0.00 3.80 2.48

Michael Lorenzen, Kansas City Royals

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 105 10.4% 64.2% 94.0 mph 33 19.2s 5 0.0%
Z-score 0.24 -0.06 0.05 0.07 1.09 0.54
pNERD -0.49 -0.03 0.02 0.07 0.00 -0.27 0.05 0.00 3.80 3.16

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