Andrew's Musings

MLB: What to watch on August 10, 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

New York Mets @ Milwaukee Brewers, 11:10a

Summary

This is one of the more watchable games on the board: a gNERD of 15.07 sits above the 95th percentile for games, driven by two high tNERD clubs and two starters who profile as interesting to follow. The Brewers arrive red-hot with a long winning streak and a lineup that has added real punch (Andrew Vaughn has been a clear lift), while Quinn Priester has been a legit revelation in Milwaukee and carries momentum into this start. Sean Manaea’s pNERD (7.38) reflects a veteran lefty who missed time early in the year with an oblique issue but has shown quality when healthy; his matchup versus Priester offers a contrast of controlled veteran craft and young-fire stuff. Team-wise, both clubs earn high marks for baserunning and run creation, though Milwaukee’s low barrel rate slightly moderates their offensive boom—while New York’s starters have struggled to reach the late innings, creating a likely bullpen-heavy finish. Between a top-tier gNERD, a hot home club, and a storyline of Manaea returning form vs. Priester’s breakout, this game is worth watching, especially for anyone who likes starting-pitcher matchups that hand the drama off to an active late-inning mix of relievers.

(A model from OpenAI generated this text using instructions, the NERD scores, and these sources: 1, 2, 3, 4, 5, 6.)

New York Mets

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat 26.4 10.3% 6.4 6.7 $332.0M 29.7 13.0
Z-score 0.55 1.42 1.08 0.35 2.14 1.00 0.63
tNERD 0.55 1.42 1.08 0.35 0.00 0.00 0.63 4.00 8.01

Milwaukee Brewers

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat 29.2 6.3% 13.6 21.7 $112.2M 27.6 -40.0
Z-score 0.60 -1.86 2.34 1.14 -0.82 -1.14 -1.95
tNERD 0.60 -1.86 2.34 1.14 0.82 1.14 0.00 4.00 8.18

Sean Manaea, New York Mets

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 87 10.9% 69.3% 91.5 mph 33 16.8s 1 0.0%
Z-score -0.83 0.20 2.14 -1.04 1.09 -1.39
pNERD 1.67 0.10 1.07 0.00 0.00 0.70 0.05 0.00 3.80 7.38

Quinn Priester, Milwaukee Brewers

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 92 10.1% 63.1% 93.9 mph 24 16.7s -15 0.0%
Z-score -0.53 -0.19 -0.41 0.05 -1.22 -1.47
pNERD 1.07 -0.09 -0.21 0.05 1.22 0.74 0.00 0.00 3.80 6.56

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Philadelphia Phillies @ Texas Rangers, 11:35a

Summary

This is a legitimately watchable game: a high-ceiling ace (Zack Wheeler) vs. a declining-but-serviceable veteran (Patrick Corbin), with the Phillies’ roster metrics skewing toward excitement and the gNERD well above average. The 13.22 gNERD lands north of today’s mean and above the historical 75th percentile, so this matchup is one of the better viewing propositions on the slate.

Wheeler’s pNERD (10.33) is elite — his xFIP- and swing‑and‑miss profile drive most of that value, and his velocity and command components suggest he can shorten games by punching guys out; that upside is the biggest reason to tune in. Corbin’s pNERD (3.93) is pedestrian: his peripherals and below‑average strike%/velocity components point toward contact and ball-in-play risk, which makes for a classic ace-versus-assembly feel rather than a back‑and‑forth slugfest. Team-wise, Philly’s tNERD (7.11) is substantially stronger than Texas’s, thanks to baserunning and batting runs, so the lineup side also favors the Phillies.

Caveat: Wheeler was bumped a few days after shoulder soreness but had clean imaging and is cleared to start — something to watch during the first inning.

(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 Payroll Age Luck Constant Total
Raw stat 36.7 9.1% 7.3 3.2 $279.5M 29.5 11.0
Z-score 0.75 0.43 1.23 0.16 1.43 0.79 0.53
tNERD 0.75 0.43 1.23 0.16 0.00 0.00 0.53 4.00 7.11

Texas Rangers

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat -57.5 8.7% 8.5 12.3 $219.7M 30.4 -29.0
Z-score -1.13 0.11 1.44 0.64 0.63 1.71 -1.42
tNERD -1.13 0.11 1.44 0.64 0.00 0.00 0.00 4.00 5.06

Zack Wheeler, Philadelphia Phillies

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 64 14.4% 64.9% 96.0 mph 35 18.5s -2 0.0%
Z-score -2.21 1.89 0.31 1.00 1.60 -0.02
pNERD 4.42 0.95 0.15 1.00 0.00 0.01 0.00 0.00 3.80 10.33

Patrick Corbin, Texas Rangers

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 98 10.8% 62.5% 91.4 mph 35 18.4s -1 0.0%
Z-score -0.17 0.15 -0.68 -1.08 1.60 -0.10
pNERD 0.35 0.07 -0.34 0.00 0.00 0.05 0.00 0.00 3.80 3.93

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Chicago Cubs @ St. Louis Cardinals, 4:10p

Summary

This game is worth a glance mostly because Sonny Gray shapes it into a bona fide pitching draw while the Cubs’ roster and team strength promise more action than the Cardinals’ tNERD suggests. Shota Imanaga’s presence softens the intrigue—he returned from a hamstring IL and was activated June 26, so he’s not an unknown but his pNERD is modest and the component mix here points to a quieter, control-oriented outing rather than fireworks. Sonny Gray, by contrast, brings the stuff and the matchup juice (his season has the strikeout volume, solid underlying metrics and the role of Cardinals’ veteran ace), which explains his much higher pNERD and makes him the main reason to tune in. The Cardinals also scratched All‑Star Brendan Donovan for precautionary groin tightness, a lineup tweak that nudges the game further toward the Cubs’ favor and slightly lowers the offensive intrigue. So: if you care about pitcher‑driven chess matches and a likely low-to-moderate scoring game with a real chance of strong peripherals (Ks, weak contact), this one is watchable; if you want big-slate fireworks, there are better options.

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

Chicago Cubs

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat 69.9 10.0% 7.6 26.1 $197.7M 30.6 -16.0
Z-score 1.42 1.17 1.29 1.37 0.33 1.91 -0.78
tNERD 1.42 1.17 1.29 1.37 0.00 0.00 0.00 4.00 9.24

St. Louis Cardinals

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat 4.7 7.8% -4.8 23.2 $135.7M 28.6 -6.0
Z-score 0.11 -0.63 -0.89 1.22 -0.50 -0.13 -0.30
tNERD 0.11 -0.63 -0.89 1.22 0.50 0.13 0.00 4.00 4.44

Shota Imanaga, Chicago Cubs

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 112 11.8% 67.9% 90.8 mph 31 18.9s -35 0.0%
Z-score 0.67 0.63 1.57 -1.36 0.58 0.30
pNERD -1.33 0.32 0.78 0.00 0.00 -0.15 0.00 0.00 3.80 3.42

Sonny Gray, St. Louis Cardinals

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 73 12.1% 66.9% 92.0 mph 35 20.4s 30 0.0%
Z-score -1.67 0.78 1.14 -0.81 1.60 1.51
pNERD 3.34 0.39 0.57 0.00 0.00 -0.76 0.05 0.00 3.80 7.40

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Toronto Blue Jays @ Los Angeles Dodgers, 1:10p

Summary

This one’s worth throwing on if you like tense pitcher-versus-lineup chess with a little thunder up the middle — it sits comfortably above average on the gNERD scale and pairs two pitchers who profile very differently. Tyler Glasnow’s extra velocity and recent strong run since returning from stints on the IL make him the more exciting arm on paper, while Eric Lauer’s game-to-game consistency and history of quieting the Dodgers keep things competitive; that contrast is the core of the watchability here. Glasnow has worked on delivery tweaks and looked healthier this year while allowing few runs in his post-IL starts. Lauer’s steady surface metrics and success against Los Angeles (and the Blue Jays’ above-average team components in batting and fielding) push the matchup’s tNERDs higher than a typical matinee. The Dodgers’ lineup still carries game-breaking bats, so even a low-scoring outing could flip quickly — yesterday’s LA offense reminded viewers it can explode. In short: the game’s gNERD (11.64) reflects a legitimately watchable duel — not must-see theater, but an appealing contrast of swing-and-miss upside vs. methodical suppression.

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

Toronto Blue Jays

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat 74.5 8.2% -4.5 28.5 $248.4M 29.6 26.0
Z-score 1.51 -0.30 -0.84 1.50 1.01 0.89 1.26
tNERD 1.51 -0.30 -0.84 1.50 0.00 0.00 1.26 4.00 7.13

Los Angeles Dodgers

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat 74.3 9.9% 0.8 -7.3 $341.0M 29.6 -7.0
Z-score 1.50 1.09 0.09 -0.39 2.26 0.89 -0.35
tNERD 1.50 1.09 0.09 -0.39 0.00 0.00 0.00 4.00 6.30

Eric Lauer, Toronto Blue Jays

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 94 9.5% 67.0% 91.7 mph 30 20.1s -30 0.0%
Z-score -0.41 -0.48 1.19 -0.95 0.32 1.27
pNERD 0.83 -0.24 0.59 0.00 0.00 -0.63 0.00 0.00 3.80 4.35

Tyler Glasnow, Los Angeles Dodgers

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 87 10.8% 59.2% 96.1 mph 31 18.6s -13 0.0%
Z-score -0.83 0.15 -2.06 1.04 0.58 0.06
pNERD 1.67 0.07 -1.03 1.04 0.00 -0.03 0.00 0.00 3.80 5.52

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Tampa Bay Rays @ Seattle Mariners, 1:10p

Summary

This is a legitimately watchable mismatch: a high-end, swing-and-miss Bryan Woo opposite an Adrian Houser profile that invites contact, with a hitting lineup (and recent power surge) on the other side—expect strikeouts, dingers and uneven defense to decide the rhythm. The gNERD (11.37) sits above the season mean and comfortably past the median, so it’s a bit more entertaining than your average game: Woo’s pNERD (7.75) flags true swing-and-miss upside (strong xFIP-, strike rate and velo), while Houser’s lower pNERD (3.70) suggests fewer Ks and more balls in play, which amplifies the Rays’ defensive shortcomings and the Mariners’ power profile. Seattle’s offense has been heating up — Julio Rodríguez and Cal Raleigh led a recent win with big homers — and the club arrives on a multi-game home streak, so there’s real momentum to their run-scoring. Those ingredients (good starter vs. contact starter, an aggressive lineup, below-average fielding on the Rays, and solid baserunning from Tampa Bay) make this a TV-friendly spot: if you like strikeout duels that turn into home-run parades or sloppy, high-leverage defensive moments, prioritize this one.

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

Tampa Bay Rays

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat -10.1 7.5% 8.6 -26.5 $89.9M 27.4 -20.0
Z-score -0.18 -0.88 1.46 -1.40 -1.12 -1.35 -0.98
tNERD -0.18 -0.88 1.46 -1.40 1.12 1.35 0.00 4.00 5.47

Seattle Mariners

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat 47.9 9.3% 0.1 -18.0 $152.8M 28.2 9.0
Z-score 0.98 0.60 -0.03 -0.95 -0.27 -0.53 0.43
tNERD 0.98 0.60 -0.03 -0.95 0.27 0.53 0.43 4.00 5.83

Adrian Houser, Tampa Bay Rays

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 102 8.3% 65.1% 94.3 mph 32 18.2s -40 0.0%
Z-score 0.07 -1.06 0.41 0.23 0.83 -0.26
pNERD -0.13 -0.53 0.21 0.23 0.00 0.13 0.00 0.00 3.80 3.70

Bryan Woo, Seattle Mariners

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 85 12.0% 67.5% 95.5 mph 25 20.4s -5 0.0%
Z-score -0.95 0.73 1.41 0.77 -0.96 1.51
pNERD 1.90 0.37 0.71 0.77 0.96 -0.76 0.00 0.00 3.80 7.75

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Boston Red Sox @ San Diego Padres, 1:10p

Summary

This is a legitimately watchable game: Dylan Cease’s high-voltage stuff (high pNERD) against a Red Sox offense built to make pitchers uncomfortable creates real strikeout-versus-contact drama, even if Brayan Bello’s profile dampens the ceiling. The gNERD (11.21) sits above the historical mean and today’s average, led by Boston’s strong tNERD (7.53) and Cease’s punchy pNERD (8.39), so expect more action than a neutral matchup would produce. Cease brings the swing-and-miss and heavy velo that drive entertaining outings, and his sub-100 xFIP- and elite swing‑and‑miss component suggest strikeouts and high-leverage moments; Bello’s pNERD (3.64) and lower swing-and-miss figures imply shorter outings and more contact, which can keep the game moving but also limit prolonged duel narratives. The Padres’ clubhouse activity—trade chatter around Cease and some rotation juggling with Michael King’s recent roster moves—adds topical intrigue beyond box-score outcomes. Overall, this is a game to pick over others if you want strikeouts, a lively Red Sox lineup, and a subplot about whether Cease stays—or becomes trade fodder—before season’s end.

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

Boston Red Sox

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat 23.8 9.9% 5.8 18.1 $191.8M 28.7 -12.0
Z-score 0.50 1.09 0.97 0.95 0.25 -0.02 -0.59
tNERD 0.50 1.09 0.97 0.95 0.00 0.02 0.00 4.00 7.53

San Diego Padres

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat 1.4 7.0% -1.7 -1.5 $209.3M 30.0 11.0
Z-score 0.05 -1.29 -0.35 -0.09 0.49 1.30 0.53
tNERD 0.05 -1.29 -0.35 -0.09 0.00 0.00 0.53 4.00 2.86

Brayan Bello, Boston Red Sox

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 102 8.6% 62.0% 95.2 mph 26 19.7s -31 0.0%
Z-score 0.07 -0.92 -0.87 0.63 -0.70 0.95
pNERD -0.13 -0.46 -0.44 0.63 0.70 -0.47 0.00 0.00 3.80 3.64

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 33 0.0%
Z-score -1.19 2.62 -0.27 1.49 0.07 1.03
pNERD 2.38 1.31 -0.13 1.49 0.00 -0.51 0.05 0.00 3.80 8.39

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Athletics @ Baltimore Orioles, 10:35a

Summary

This is a middling watch: it’s not a must-see pitcher’s duel, but a fresh A’s arm against a strikeout-y lefty and a mismatch between an up-and-coming Oakland offense and a slumping Baltimore lineup gives this game some low-key intrigue. The gNERD (10.22) sits almost exactly at the historical median, which matches what you see in the components — Oakland’s team NERD is high because they’re young, cheap and swinging the bat well, and their positive “luck” component suggests they’re due to perform even better, while Baltimore’s tNERD is depressed by weak offense and shaky defense. Luis Morales is the X-factor: he just made his MLB debut and carries prospect buzz for his slider and feel, but his pNERD is 0 because there’s essentially no big-league sample to lean on, so watching him is more about scouting upside than predictability. Cade Povich is the safer analytical story — he misses bats (high K/9) but has shaky run prevention this year and a near-average xFIP-, so expect strikeouts and inconsistency. If you want rookie intrigue and offense over pitching purity, this is worth tuning into; if you crave elite starters, skip it.

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

Athletics

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat 26.3 8.3% 0.8 -15.8 $77.1M 27.6 50.0
Z-score 0.55 -0.22 0.09 -0.84 -1.29 -1.14 2.43
tNERD 0.55 -0.22 0.09 -0.84 1.29 1.14 2.00 4.00 8.01

Baltimore Orioles

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat -12.4 9.2% -4.5 -20.4 $167.6M 29.2 -11.0
Z-score -0.23 0.52 -0.84 -1.08 -0.07 0.49 -0.54
tNERD -0.23 0.52 -0.84 -1.08 0.07 0.00 0.00 4.00 2.44

Luis Morales, Athletics

No detailed stats available

Cade Povich, Baltimore Orioles

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 95 9.0% 62.3% 92.2 mph 25 18.0s 35 0.0%
Z-score -0.35 -0.72 -0.78 -0.72 -0.96 -0.42
pNERD 0.71 -0.36 -0.39 0.00 0.96 0.21 0.05 0.00 3.80 4.98

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Houston Astros @ New York Yankees, 10:35a

Summary

Not must‑see TV, but worth flipping to if you like watching a dominant lefty carve up an overmatched lineup — and the Yankees' offense makes the outcome feel likely. The gNERD sits basically dead average (10.14 vs historical mean 10.11), so this is more a pitching‑mismatch game than a high‑drama duel. Max Fried is the real draw: his pNERD and excellent xFIP- point to a reliable, contact‑suppressing outing and the broader coverage expects him to limit runs and rack strikeouts. Jason Alexander’s pNERD is low and the scouting notes and betting markets line up — he’s been hittable and command‑fragile, which amplifies the gap. The Yankees’ team profile (high barrel rate, run creation) boosts their tNERD and explains why the book favors New York; Houston’s lower tNERD and middling batted‑ball profile make them unlikely to force fireworks unless Fried has an off day. Taken together, this is a comfortably watchable game if you like polished pitching vs. lineup firepower, but not the kind of even‑odds, back‑and‑forth spectacle that ranks among the day’s best.

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

Houston Astros

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat 18.0 7.6% -3.1 6.1 $221.9M 29.0 28.0
Z-score 0.38 -0.79 -0.59 0.32 0.66 0.28 1.36
tNERD 0.38 -0.79 -0.59 0.32 0.00 0.00 1.36 4.00 4.67

New York Yankees

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat 72.8 11.2% -3.2 6.7 $290.9M 29.1 5.0
Z-score 1.47 2.15 -0.61 0.35 1.58 0.38 0.24
tNERD 1.47 2.15 -0.61 0.35 0.00 0.00 0.24 4.00 7.60

Jason Alexander, Houston Astros

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 109 10.0% 61.9% 91.4 mph 32 17.3s 36 0.0%
Z-score 0.49 -0.24 -0.95 -1.08 0.83 -0.99
pNERD -0.97 -0.12 -0.47 0.00 0.00 0.49 0.05 0.00 3.80 2.78

Max Fried, New York Yankees

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 82 10.6% 63.1% 94.1 mph 31 20.5s -13 0.0%
Z-score -1.13 0.05 -0.42 0.14 0.58 1.59
pNERD 2.26 0.03 -0.21 0.14 0.00 -0.80 0.00 0.00 3.80 5.22

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Los Angeles Angels @ Detroit Tigers, 10:40a

Summary

Mildly worth your time: Detroit’s lineup and a healthy Casey Mize give this game some bite, but an uninspiring Angels offense and a shaky Kochanowicz starting profile keep it from being must-see. The gNERD (9.64) sits just below the season median, so expect a watchable but not headline-grabbing pitching duel — mainly because the matchup is lopsided on paper: Detroit’s team NERD is high (8.33) thanks to strong offense and baserunning, while the Angels’ tNERD is one of the lower marks (2.77), meaning more action will likely come from Detroit’s side (team context noted). Casey Mize returned from a hamstring IL stint and has been cleared to start, which raises the ceiling here given his above-average pNERD and recent strikeout profile. Jack Kochanowicz’s season has been inconsistent — he’s shown flashes but also a lately shaky outing — which helps explain his low pNERD and why hitters might be favored. Off-the-field context nudges interest: Detroit remains a division leader with tangible storylines around sustaining its form, while the Angels have mixed reinforcements (trout/Moore returns) but limited pitching upside today. In short, tune in if you like watching a strong lineup press against a borderline starter and the drama of a club trying to hold a division lead; don’t expect a classic pitchers’ duel.

(A model from OpenAI generated this text using instructions, the NERD scores, and these sources: 1, 2, 3, 4, 5, 6.)

Los Angeles Angels

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat -23.6 10.7% -2.0 -40.1 $203.8M 29.2 -11.0
Z-score -0.45 1.74 -0.40 -2.12 0.41 0.49 -0.54
tNERD -0.45 1.74 -0.40 -2.12 0.00 0.00 0.00 4.00 2.77

Detroit Tigers

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat 28.2 9.8% 5.0 8.3 $148.2M 27.6 -20.0
Z-score 0.58 1.01 0.83 0.43 -0.33 -1.14 -0.98
tNERD 0.58 1.01 0.83 0.43 0.33 1.14 0.00 4.00 8.33

Jack Kochanowicz, Los Angeles Angels

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 119 9.1% 61.9% 95.6 mph 24 18.7s 24 0.0%
Z-score 1.08 -0.67 -0.95 0.82 -1.22 0.14
pNERD -2.17 -0.34 -0.47 0.82 1.22 -0.07 0.05 0.00 3.80 2.83

Casey Mize, Detroit Tigers

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 99 10.5% 66.4% 94.6 mph 28 17.8s -13 0.0%
Z-score -0.11 0.00 0.93 0.36 -0.19 -0.58
pNERD 0.23 0.00 0.47 0.36 0.19 0.29 0.00 0.00 3.80 5.34

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Cincinnati Reds @ Pittsburgh Pirates, 10:35a

Summary

Not a can’t-miss: the game rates as middling watchability, more grind than fireworks, but it has some rookie intrigue worth a look. If you want upside and storyline over steady excitement, Mike Burrows’ projection as a mid-90s, multi‑pitch prospect and Zack Littell’s recent strong debut give you that angle.

The gNERD of 8.79 sits below today’s average and the season’s median, driven by two teams with weak tNERDs (Reds 3.79, Pirates 2.70) and poor offensive profiles, so don’t expect a run‑fest. Littell’s pNERD is modest and he’s coming off a 7‑inning, 8‑K debut for Cincinnati after the trade, which makes him interesting but not overpowering. Burrows’ higher pNERD reflects youth, velocity and a fuller pitch mix that can produce swing-and-miss and big outcomes, though he was knocked around in a recent start — so he’s entertaining because he’s volatile. The daily matchup lists Littell vs. Burrows as the probables, so if you like upside, follow Burrows; if you want steady polish, this game is lower priority.

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

Cincinnati Reds

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat -33.4 7.1% 6.3 -3.9 $115.7M 28.7 -21.0
Z-score -0.65 -1.20 1.06 -0.21 -0.77 -0.02 -1.03
tNERD -0.65 -1.20 1.06 -0.21 0.77 0.02 0.00 4.00 3.79

Pittsburgh Pirates

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat -94.2 8.1% -7.3 13.9 $88.9M 28.4 2.0
Z-score -1.86 -0.38 -1.33 0.73 -1.13 -0.33 0.09
tNERD -1.86 -0.38 -1.33 0.73 1.13 0.33 0.09 4.00 2.70

Zack Littell, Cincinnati Reds

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 102 9.5% 67.8% 91.9 mph 29 18.0s -17 0.0%
Z-score 0.07 -0.48 1.54 -0.86 0.07 -0.42
pNERD -0.13 -0.24 0.77 0.00 0.00 0.21 0.00 0.00 3.80 4.41

Mike Burrows, Pittsburgh Pirates

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 97 10.9% 64.0% 95.4 mph 25 17.0s 8 0.0%
Z-score -0.23 0.20 -0.07 0.73 -0.96 -1.23
pNERD 0.47 0.10 -0.04 0.73 0.96 0.61 0.05 0.00 3.80 6.68

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Miami Marlins @ Atlanta Braves, 10:35a

Summary

Not a can't-miss pitching duel — more like two serviceable arms and a lineup mismatch that make this a low-to-medium priority watch. The gNERD of 8.29 sits below today's average and the season's median, so this game ranks a touch below the day's most compelling options.

Both starters explain why: Cal Quantrill's underlying stuff has been middling (high xFIP-, low swinging‑strike rates) and he was hit hard in his last turn, getting tagged for seven runs against Houston. Joey Wentz has flashed useful length and a decent early ERA with Atlanta but was knocked around for five runs in his most recent start; he's a reclamation/rotation‑depth piece rather than a live arm that drives big viewership.

Team context nudges interest slightly: Miami's tNERD is higher thanks to youth and the quirky profile that makes them unpredictable, while Atlanta's roster has been thinned by injuries and streaky offense even as fielding and fortunate results have propped them up.

If you want contact, situational at‑bats, and the possibility of a long, low‑strikeout game with defensive plays deciding it, tune in; if you want fireworks or a strikeout spectacle, there are better bets.

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

Miami Marlins

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat -12.6 8.0% -2.5 4.4 $67.3M 26.8 6.0
Z-score -0.23 -0.47 -0.49 0.23 -1.42 -1.96 0.29
tNERD -0.23 -0.47 -0.49 0.23 1.42 1.96 0.29 4.00 6.71

Atlanta Braves

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat -10.2 9.2% -2.9 14.9 $216.2M 29.4 25.0
Z-score -0.18 0.52 -0.56 0.78 0.58 0.69 1.21
tNERD -0.18 0.52 -0.56 0.78 0.00 0.00 1.21 4.00 5.77

Cal Quantrill, Miami Marlins

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 110 8.8% 62.5% 93.7 mph 30 18.8s 12 0.0%
Z-score 0.55 -0.82 -0.67 -0.04 0.32 0.22
pNERD -1.09 -0.41 -0.34 0.00 0.00 -0.11 0.05 0.00 3.80 1.90

Joey Wentz, Atlanta Braves

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 112 11.1% 63.6% 93.9 mph 27 20.6s 16 0.0%
Z-score 0.67 0.29 -0.23 0.05 -0.45 1.67
pNERD -1.33 0.15 -0.11 0.05 0.45 -0.84 0.05 0.00 3.80 2.21

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Cleveland Guardians @ Chicago White Sox, 11:10a

Summary

Mildly watchable: this is the sort of game where steady starting pitching — not fireworks — is the selling point, and Cleveland’s Slade Cecconi is the only real reason to tune in. The gNERD of 8.23 sits below today’s slate average and well under the historical median, so this isn’t a must-watch on paper; still, the split pNERD profile (Cecconi 5.20 vs. Davis Martin 3.80) nudges the interest meter toward a low-key pitching duel where one looming storyline is whether Cecconi’s recent run of length (he’s pitched six or more innings in a string of starts) continues to blunt a White Sox offense that grades poorly by the tNERD components. Cleveland’s team metrics show weak barrel and hitting results but solid defense and a younger roster tilt, while Chicago’s tNERD is dragged down by very poor batting and fielding marks, so expect a low-to-moderate run total and a game decided by a single bullpen inning or two. If you prize stable, inning-eating starts and modest strategic intrigue, this is worth catching; if you want action or high upside, there are better choices.

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

Cleveland Guardians

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat -55.3 6.8% 1.8 17.2 $102.3M 27.5 -20.0
Z-score -1.09 -1.45 0.27 0.90 -0.95 -1.25 -0.98
tNERD -1.09 -1.45 0.27 0.90 0.95 1.25 0.00 4.00 4.83

Chicago White Sox

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat -71.3 8.0% -3.4 -25.8 $79.0M 27.5 -9.0
Z-score -1.41 -0.47 -0.64 -1.37 -1.26 -1.25 -0.44
tNERD -1.41 -0.47 -0.64 -1.37 1.26 1.25 0.00 4.00 2.63

Slade Cecconi, Cleveland Guardians

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 97 10.1% 65.0% 94.1 mph 26 18.5s -4 0.0%
Z-score -0.23 -0.19 0.36 0.14 -0.70 -0.02
pNERD 0.47 -0.09 0.18 0.14 0.70 0.01 0.00 0.00 3.80 5.20

Davis Martin, Chicago White Sox

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 103 9.7% 62.8% 93.5 mph 28 17.2s -2 0.0%
Z-score 0.13 -0.38 -0.55 -0.13 -0.19 -1.07
pNERD -0.25 -0.19 -0.28 0.00 0.19 0.53 0.00 0.00 3.80 3.80

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Colorado Rockies @ Arizona Diamondbacks, 1:10p

Summary

This is more of a low-key TV choice than a must-watch: Arizona’s stronger lineup and Pfaadt’s higher pNERD inject some life, but Colorado’s anemic tNERD and Tanner Gordon’s meek pNERD keep the game under the day’s usual appeal. If you want offense without elite pitching duels, this one will do — but don’t expect fireworks.

The numbers tell the story: a gNERD of 7.14 sits below today’s mean (~9.97) and well under the series median, driven by a 1.50 Rockies tNERD and Gordon’s 1.29 pNERD; Brandon Pfaadt’s 4.38 pNERD and Arizona’s 7.12 tNERD rescue the game’s intrigue but don’t vault it into “can’t-miss” territory. Gordon has flashed strike-throwing and a few promising starts but remains inconsistent and light on strikeouts, which explains his low pNERD. Pfaadt’s recent run of wins and steady innings supply real value here and makes Arizona the safer bet for competent starting pitching. Colorado’s lineup and run prevention have been poor lately, which suppresses volatility and big-spot drama unless the D-backs’ bullpen or slumping bats flip the script.

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

Colorado Rockies

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat -111.2 8.4% -6.6 -28.2 $125.9M 27.9 22.0
Z-score -2.20 -0.14 -1.21 -1.49 -0.63 -0.84 1.07
tNERD -2.20 -0.14 -1.21 -1.49 0.63 0.84 1.07 4.00 1.50

Arizona Diamondbacks

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat 51.8 9.0% 3.4 9.3 $189.5M 29.5 14.0
Z-score 1.05 0.35 0.55 0.48 0.22 0.79 0.68
tNERD 1.05 0.35 0.55 0.48 0.00 0.00 0.68 4.00 7.12

Tanner Gordon, Colorado Rockies

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 122 7.3% 66.7% 91.8 mph 27 19.1s 17 0.0%
Z-score 1.26 -1.55 1.06 -0.90 -0.45 0.46
pNERD -2.53 -0.77 0.53 0.00 0.45 -0.23 0.05 0.00 3.80 1.29

Brandon Pfaadt, Arizona Diamondbacks

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 99 9.5% 64.9% 93.4 mph 26 19.3s 20 0.0%
Z-score -0.11 -0.48 0.30 -0.18 -0.70 0.62
pNERD 0.23 -0.24 0.15 0.00 0.70 -0.31 0.05 0.00 3.80 4.38

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Washington Nationals @ San Francisco Giants, 1:05p

Summary

This is more of a pitching curiosity than a can’t-miss matchup: MacKenzie Gore’s high-strikeout profile makes the game watchable if you like swing-and-miss upside, but the overall game NERD is below average, so don’t expect a fireworks show.

Gore’s pNERD is the more interesting figure here — he’s one of the more strikeout-heavy young arms on the slate, which explains his 6.93 pNERD — but he’s scuffled lately, with control issues and ugly recent lines that temper the upside. Justin Verlander brings the veteran storyline and name recognition, but his season numbers and modest pNERD suggest he’s not the same dominant force he once was; this looks like a matchup that favors strikeouts over sustained dominance. The teams split the early games of the series, with the Nationals eking out a daytime win recently, so there’s some competitive context but not a hot rivalry to juice interest. With both teams’ tNERDs low and the gNERD (6.57) well under typical daily and historical medians, pick this if you want to watch Gore’s arsenal; otherwise it’s a lower-priority game.

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

Washington Nationals

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat -36.7 7.7% -3.6 -37.2 $115.9M 27.5 -25.0
Z-score -0.71 -0.71 -0.68 -1.97 -0.77 -1.25 -1.22
tNERD -0.71 -0.71 -0.68 -1.97 0.77 1.25 0.00 4.00 1.94

San Francisco Giants

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat -14.6 7.5% -7.6 9.1 $195.3M 29.3 -10.0
Z-score -0.27 -0.88 -1.38 0.47 0.30 0.59 -0.49
tNERD -0.27 -0.88 -1.38 0.47 0.00 0.00 0.00 4.00 1.94

MacKenzie Gore, Washington Nationals

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 88 13.4% 62.8% 95.3 mph 26 19.2s 14 0.0%
Z-score -0.77 1.41 -0.56 0.68 -0.70 0.54
pNERD 1.55 0.70 -0.28 0.68 0.70 -0.27 0.05 0.00 3.80 6.93

Justin Verlander, San Francisco Giants

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 115 11.0% 64.8% 94.2 mph 42 19.1s -9 0.0%
Z-score 0.85 0.25 0.28 0.18 3.40 0.46
pNERD -1.69 0.12 0.14 0.18 0.00 -0.23 0.00 0.00 3.80 2.32

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Kansas City Royals @ Minnesota Twins, 10:05a

Summary

This is a snoozer by the numbers: a bottom-tier gNERD driven by two low-pitcher NERD starters and meek team offense, so don’t expect a high-strikeout, high-drama affair. The game’s appeal is mostly narrative thin—Ryan Bergert is a young arm freshly acquired from San Diego who looked tidy in a Royals debut (5 2/3 IP, 2 runs allowed), but his pNERD (1.92) and underlying strike metrics are weak; José Ureña is a veteran journeyman the Twins recently added and has been shuttling between roles, producing modest results and a modest pNERD (2.78).

Team NERDs sit below today’s averages (Royals 3.37, Twins 3.53) because Kansas City’s offense grades poorly while Minnesota’s lineup has been shuffled after a roster overhaul; the Royals’ luck component is notable enough (≈1.16) to suggest some positive regression for them, but that’s not a ticket to excitement. Overall, expect a low-event pitcher’s duel with a bit of prospect interest (Bergert) and roster-story intrigue (Twins turnover), not a must-watch fireworks display.

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

Kansas City Royals

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat -67.8 7.5% -2.9 7.7 $130.0M 28.8 24.0
Z-score -1.34 -0.88 -0.56 0.40 -0.58 0.08 1.16
tNERD -1.34 -0.88 -0.56 0.40 0.58 0.00 1.16 4.00 3.37

Minnesota Twins

Batting runs Barrel % Baserunning runs Fielding runs Payroll Age Luck Constant Total
Raw stat -3.9 9.0% -7.3 -9.1 $145.1M 28.8 14.0
Z-score -0.06 0.35 -1.33 -0.49 -0.37 0.08 0.68
tNERD -0.06 0.35 -1.33 -0.49 0.37 0.00 0.68 4.00 3.53

Ryan Bergert, Kansas City Royals

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 119 8.3% 62.9% 93.5 mph 25 18.2s -49 0.0%
Z-score 1.08 -1.06 -0.53 -0.13 -0.96 -0.26
pNERD -2.17 -0.53 -0.26 0.00 0.96 0.13 0.00 0.00 3.80 1.92

José Ureña, Minnesota Twins

xFIP- SwStr% Strike % Velocity Age Pace Luck KN% Constant Total
Raw stat 129 8.8% 68.1% 96.4 mph 33 16.8s 1 0.0%
Z-score 1.68 -0.82 1.66 1.18 1.09 -1.39
pNERD -3.37 -0.41 0.83 1.18 0.00 0.70 0.05 0.00 3.80 2.78

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