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Weekly Player Play Probability

This chart tracks how many injury episodes a team has piled up through each week of a season, next to the league average for the same injury. One line is the team's running total. The other is the league average, which is the league-wide total divided by 32. When the team line sits above the league line, that team has had more of that injury than a typical team so far. Below it, fewer.
It's built for in-season reads. Is a team's ankle run just bad luck, or a real outlier? Is a rash of hamstring tags something to worry about, or normal for this point in the season? You can check that at any week, for any season in the data.
How to use it: Set the Team, Injury, and Year filters before you run it. If one is blank, the lines mix several combinations together.
How it's built:
<ul dir="ltr">
<li>An episode is one continuous stretch of an injury on a team's report. A gap of two or more weeks starts a new episode.</li>
<li>Counts are cumulative. Each week's value is the total episodes from the start of the injury's first appearance that season through that week.</li>
<li>An episode is an injury designation on the report. It doesn't mean the player missed a game.</li>
<li>Non-injury designations (rest, illness, personal matter, and similar) are excluded. Left and right are combined, so Left Ankle and Right Ankle both count as Ankle. Multi-injury entries such as "Ankle, Knee" are their own separate categories.</li>
<li>The league average is only meaningful once enough episodes exist. The flag reads "Sufficient Data" once the league total for that injury and week reaches 32.</li></ul><div> </div><div><span style="--tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-gradient-from-position: ; --tw-gradient-via-position: ; --tw-gradient-to-position: ; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgb(59 130 246/0.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; --tw-contain-size: ; --tw-contain-layout: ; --tw-contain-paint: ; --tw-contain-style: ; font-size: 12pt; font-variant: normal; white-space: pre-wrap; background-color: transparent; font-family: Arial, sans-serif; color: rgb(0, 0, 0); vertical-align: baseline;">Methodology developed and reviewed by Dr. Kyle Trimble PT, DPT.<span style="--tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-gradient-from-position: ; --tw-gradient-via-position: ; --tw-gradient-to-position: ; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgb(59 130 246/0.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; --tw-contain-size: ; --tw-contain-layout: ; --tw-contain-paint: ; --tw-contain-style: ; font-variant: normal; white-space: pre-wrap; background-color: transparent; font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); vertical-align: baseline;">&nbsp;</div>

Team vs League Injury Episodes - Cumulative by Week

What is the probablity that a player plays based on a standard week of participation?

Wed-Thu-Fri Practice Participation Probability

Wednesday and Thursday Practice Probability Play 

Wed-Thu Practice Participation Probability

Wednesday Practice Participation Probability

Average Games Missed by Position and Injury

Positional Injuries Games Missed Average

Looks at what the chances of a player playing on a short week

See how a player's participation during an NFL short week (a Monday/Tuesday/Wednesday practice schedule, typically ahead of a Thursday game) translates into their actual likelihood of playing that week. This insight is built from isolating short-week practice patterns specifically — the compressed prep window that changes how injury designations play out compared to a standard week. Non-injury absences (rest days, illness, personal matters, suspensions) are filtered out, so every result reflects genuine injury-driven practice participation, not administrative noise. Select a team and their practice designation's that week (Full, Limited, or DNP) to get the real-world play rate teams have shown from that starting point. Results flagged "Insufficient Data" indicate a sample too small to trust — shown transparently rather than hidden, so you always know what you're working with. Methodology developed and reviewed by Dr. Kyle Trimble PT, DPT.&nbsp;<div> </div>

Mon-Tues-Wed Practice Participation Probability

Monday & Tuesday Practice Play Probability based on participation

See how a player's Monday and Tuesday practice status during an NFL short week (a Monday/Tuesday/Wednesday practice schedule, typically ahead of a Thursday game) translates into their actual likelihood of playing that week. This insight is built from isolating short-week practice patterns specifically — the compressed prep window that changes how injury designations play out compared to a standard week. Non-injury absences (rest days, illness, personal matters, suspensions) are filtered out, so every result reflects genuine injury-driven practice participation, not administrative noise. Select a team and a Monday/Tuesday practice designation (Full, Limited, or DNP) to get the real-world play rate teams have shown from that starting point. Results flagged "Insufficient Data" indicate a sample too small to trust — shown transparently rather than hidden, so you always know what you're working with. Methodology developed and reviewed by Dr. Kyle Trimble PT, DPT.&nbsp;<div> </div>

Monday-Tuesday Practice Play Probability

Determining the chances a player suits up on Monday, the beginning of a short week

See how a player's Monday practice status during an NFL short week (a Monday/Tuesday/Wednesday practice schedule, typically ahead of a Thursday game) translates into their actual likelihood of playing that week.
This insight is built from isolating short-week practice patterns specifically — the compressed prep window that changes how injury designations play out compared to a standard week. Non-injury absences (rest days, illness, personal matters, suspensions) are filtered out, so every result reflects genuine injury-driven practice participation, not administrative noise.
Select a team and a Monday practice designation (Full, Limited, or DNP) to get the real-world play rate teams have shown from that starting point. Results flagged "Insufficient Data" indicate a sample too small to trust — shown transparently rather than hidden, so you always know what you're working with. <span style="--tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-gradient-from-position: ; --tw-gradient-via-position: ; --tw-gradient-to-position: ; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgb(59 130 246/0.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; --tw-contain-size: ; --tw-contain-layout: ; --tw-contain-paint: ; --tw-contain-style: ; font-size: 12pt; font-variant: normal; white-space: pre-wrap; background-color: transparent; font-family: Arial, sans-serif; color: rgb(0, 0, 0); vertical-align: baseline;">Methodology developed and reviewed by Dr. Kyle Trimble PT, DPT.<span style="--tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-gradient-from-position: ; --tw-gradient-via-position: ; --tw-gradient-to-position: ; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgb(59 130 246/0.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; --tw-contain-size: ; --tw-contain-layout: ; --tw-contain-paint: ; --tw-contain-style: ; font-variant: normal; white-space: pre-wrap; background-color: transparent; font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); vertical-align: baseline;">&nbsp;

Monday Practice Participation Probability

A single, easy-to-read number (0–100) that captures how banged-up an NFL team's roster that particular week before the inactives came out. Built from that week's official injury report, the score weights each player's designation (Out, Doubtful, Questionable) by the relative importance of their position — a starting QB or CB listed as Out drags the score down far more than a long snapper — so the number reflects true roster impact, not just a raw count of names on the report. A score near 100 means a clean bill of health. Scores dropping into the 40s, 30s, or lower flag a team dealing with significant, high-value injuries that could affect performance that week. Developed with a physical therapist's lens on positional value and injury severity, this score gives analysts and researchers a fast way to gauge a team's true health status beyond the surface-level injury report — without having to parse individual designations position by position. <span style="font-size: 12pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-variant-ligatures: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; vertical-align: baseline; white-space: pre-wrap;">Methodology developed and reviewed by Dr. Kyle Trimble PT, DPT.<span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); font-variant-ligatures: normal; font-variant-alternates: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; vertical-align: baseline; white-space: pre-wrap;">&nbsp;

Weekly Health Score

Chronological play-by-play timeline for individual NFL games in the 2026 NFL season

Play-by-play data for the 2026 NFL season, updated daily as games are played. Pull any game as a CSV and drop it straight into a model, a backtest, or a recap pipeline. Every row is one event, in the order it happened: kickoffs, drives, passes, rushes, play outcomes, touchdowns, field goals, penalties, timeouts, fumbles, injuries, punts, possession changes, one- and two-point conversions, safeties and overtime. Pre-game context, including weather, the coin toss and which direction each team is defending, sits ahead of the first quarter, and the halftime switch lands at the right point in the sequence. Each row carries the game, kickoff time, event category, game clock, quarter, a plain-English summary of what happened, and the running score at that moment. The MIN field represents the NFL game clock as a count-down clock within the current quarter or period, decreasing as the period progresses rather than counting continuously upwards across the game. Summaries name the teams involved, penalty type and yardage, possession, drive outcome and scoring outcome. Running scores account for touchdowns, field goals, safeties and conversions, with adjustments applied where needed so the score stays accurate start to finish. Built from the TXODDS Scores feed, on the same schema as the 2024 and 2025 seasons, so anything built against the archive runs against the live season with no remapping. Good for: in-game win probability models, drive-level analysis, automated recaps, training agents on real game sequences, and live dashboards. A catalog of every 2026 game in the order it was played is available at&nbsp;https://tinyurl.com/NFL-Scores-2026&nbsp; For additional inquiries, please email hello@txodds.com.

NFL 2026 Play-by-Play Data

Txodds Com

ENDPOINT

What is the percentage at which a doubtful player plays?

A team's doubtful tag is one of the measurable differences between teams in the NFL, some organizations never play a doubtful player while others use the designation as a smokescreen but the player actually suits up, and knowing the difference can help you navigate uncertainty.&nbsp;<div style="--tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-gradient-from-position: ; --tw-gradient-via-position: ; --tw-gradient-to-position: ; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgb(59 130 246/0.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; --tw-contain-size: ; --tw-contain-layout: ; --tw-contain-paint: ; --tw-contain-style: ; color: rgba(47, 43, 61, 0.9);"><br style="--tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-gradient-from-position: ; --tw-gradient-via-position: ; --tw-gradient-to-position: ; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgb(59 130 246/0.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; --tw-contain-size: ; --tw-contain-layout: ; --tw-contain-paint: ; --tw-contain-style: ;"></div><div style="--tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-gradient-from-position: ; --tw-gradient-via-position: ; --tw-gradient-to-position: ; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgb(59 130 246/0.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; --tw-contain-size: ; --tw-contain-layout: ; --tw-contain-paint: ; --tw-contain-style: ; color: rgba(47, 43, 61, 0.9);">Our query tool lets you pull the exact rate at which any team's doubtful players actually take the field, giving you a data-backed read on how much to trust a team's injury designations week after week. Look at the breakdown by team, year, and even position which gives you answers that simply didn’t<span style="--tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-gradient-from-position: ; --tw-gradient-via-position: ; --tw-gradient-to-position: ; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgb(59 130 246/0.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; --tw-contain-size: ; --tw-contain-layout: ; --tw-contain-paint: ; --tw-contain-style: ; text-size-adjust: 100%;"> exist before.&nbsp;<div style="--tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-gradient-from-position: ; --tw-gradient-via-position: ; --tw-gradient-to-position: ; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgb(59 130 246/0.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; --tw-contain-size: ; --tw-contain-layout: ; --tw-contain-paint: ; --tw-contain-style: ;"><br style="--tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-gradient-from-position: ; --tw-gradient-via-position: ; --tw-gradient-to-position: ; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgb(59 130 246/0.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; --tw-contain-size: ; --tw-contain-layout: ; --tw-contain-paint: ; --tw-contain-style: ;"></div><div style="--tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-gradient-from-position: ; --tw-gradient-via-position: ; --tw-gradient-to-position: ; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgb(59 130 246/0.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; --tw-contain-size: ; --tw-contain-layout: ; --tw-contain-paint: ; --tw-contain-style: ;"><span id="docs-internal-guid-a4f202f3-7fff-6c50-2228-ff462879ef50" style="--tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-gradient-from-position: ; --tw-gradient-via-position: ; --tw-gradient-to-position: ; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgb(59 130 246/0.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; --tw-contain-size: ; --tw-contain-layout: ; --tw-contain-paint: ; --tw-contain-style: ;"><p dir="ltr" style="--tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-gradient-from-position: ; --tw-gradient-via-position: ; --tw-gradient-to-position: ; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgb(59 130 246/0.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; --tw-contain-size: ; --tw-contain-layout: ; --tw-contain-paint: ; --tw-contain-style: ; margin: 0pt 0px; line-height: 1.38;"><span style="--tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-gradient-from-position: ; --tw-gradient-via-position: ; --tw-gradient-to-position: ; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgb(59 130 246/0.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; --tw-contain-size: ; --tw-contain-layout: ; --tw-contain-paint: ; --tw-contain-style: ; font-size: 12pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">Methodology developed and reviewed by Dr. Kyle Trimble PT, DPT.<span style="--tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-gradient-from-position: ; --tw-gradient-via-position: ; --tw-gradient-to-position: ; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgb(59 130 246/0.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; --tw-contain-size: ; --tw-contain-layout: ; --tw-contain-paint: ; --tw-contain-style: ; font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">&nbsp;</div></div>

Team Doubtful Rates By Year

A free, representative 100-term sample of the 2,361-term US health search trends dataset, with the identical 15-column schema and the full 72-month interest series on every row. Covers all 22 topical categories, all seven patient-journey stages and all three trend-strength tiers. Deliberately not a top-100 cut - the strength mix tracks the full product, so what you evaluate is what you buy.


<h3>What this is</h3>
100 rows drawn from the paid product, with the same 15 columns and the
same meanings. It exists so you can judge the classification quality and
the shape of the trend data before spending anything.

<h3>How the 100 were chosen</h3>
A systematic spread across the full trend-strength ranking, topped up so
every value of every classification appears at least once. Facet
representatives are picked pseudo-randomly within each facet rather than by
highest strength, so the sample does not look stronger than the product.
The selection is deterministic: this sample does not change between
requests.

<h3>What it is honest about</h3>
<ul>
 <li>Strength mix - 27% Strong, 47% Solid, 26% Moderate here,
 against 20% / 50% / 30% in the full product. Within sampling noise at
 n=100, not a flattered cut.</li>
 <li>Contamination is flagged, not hidden. 3 rows carry
 <code>is_veterinary</code> and 11 carry a <code>qa_note</code>, at
 roughly product rates. The full dataset has a small Pet &amp; Animal
 Health category (17 terms) and an Other / Miscellaneous residual (16),
 both excludable.</li>
 <li>Every term in this dataset is growing - direction is constant
 by construction, because the set is filtered for durable growth. The
 differentiating field is strength, not direction.</li>
</ul>

<h3>The full product</h3>
2,361 terms, same schema, one request, CSV export enabled.

US Health Search Trends - Free 100-Row Sample

AP Analytics

A single, easy-to-read number (0–100) that captures how banged-up an NFL team's roster was historically in any given week since 2016. Built from that week's official injury report, the score weights each player's designation (Out, Doubtful, Questionable) by the relative importance of their position — a starting QB or CB listed as Out drags the score down far more than a long snapper — so the number reflects true roster impact, not just a raw count of names on the report. 
A score near 100 means a clean bill of health. Scores dropping into the 40s, 30s, or lower flag a team dealing with significant, high-value injuries that could affect performance that week. Developed with a physical therapist's lens on positional value and injury severity, this score gives analysts and researchers a fast way to gauge a team's true health status beyond the surface-level injury report — without having to parse individual designations position by position. <span style="font-size:12pt;font-family:Arial,sans-serif;color:#000000;background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">Methodology developed and reviewed by Dr. Kyle Trimble PT, DPT.<span style="font-size:11pt;font-family:Arial,sans-serif;color:#000000;background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">&nbsp;

Historical Weekly Health Score

Queryable feed of model-derived win/draw/loss probabilities, expected goals, most-likely score, over-2.5 and both-teams-to-score for every fixture pairing. Filter by team or league. Refreshed daily.

Win/draw/loss probabilities from a time-weighted Dixon-Coles model (Poisson goals with the low-score correction, 180-day half-life) — model-derived, not copied bookmaker odds. Each row gives both sides' expected goals, the most-likely scoreline and its probability, P(over 2.5 goals), and P(both teams to score), for every home/away pairing in the leagues we cover. Filter by team or league to pull a single fixture or a whole division. Method and assumptions are documented so you can explain every number you publish. Data: football-data.co.uk. Model: Arilytics.

Soccer Match Outcome Probabilities

Arilytics

IDiyas is a professional recognition platform and comprehensive patent database designed to celebrate inventors and track innovation by providing reliable, real-time statistics on United States Patent and Trademark Office (USPTO) filings. They sell Lead Generation and Market Intelligence services, including access to a database of over 5.5 million inventors, as well as "Pro" memberships that offer verified inventor badges, patent alerts, and the ability to list patents for sale or licensing. What makes IDiyas assets unique is their "inventor-first" focus, which transforms raw patent data into a searchable "Social Network of Innovation" where human expertise is linked directly to intellectual property, creating a high-trust marketplace for subject matter experts and liquid IP assets.

Top Cities by HI State in 2025: Hawaii Patent &amp; Innovation HubsThis interactive visualization and dataset offer a localized breakdown of innovation activity across the state of Hawaii (HI) for the 2025 fiscal year. The chart ranks Hawaiian cities and research centers by their volume of granted patents, mapping out the island state's primary technology hubs.Key Insights &amp; Data Points Included:<ul data-path-to-node="6,3"><li>Hawaii Regional Tech Clusters: Volume metrics tracking patent filings in key HI urban and research hubs (including Honolulu, Hilo, Kapolei, Kailua-Kona, and Pearl City).</li><li>Specialized Field Activity: Insights into patent density within clean energy, sustainable agriculture, marine biology/ocean engineering, biotech, and defense-related technologies.</li><li>Academic &amp; Private R&amp;D Footprint: Analysis of research outputs driven by major state institutions (such as the University of Hawaiʻi System) and local commercial incubators.</li><li>Structured Regional Data: Clean, 2025 baseline dataset ready for island-economy research, regional legal operations, and B2B competitive intelligence.</li></ul>Who is this for?
Ideal for legal tech analysts, IP attorneys, regional economic development agencies, corporate R&amp;D strategists, and venture capital firms monitoring clean-tech and Pacific-region technology developments.

Top cities by HI state in 2025

Top Cities by WY State in 2025: Wyoming Patent &amp; Innovation HubsThis interactive visualization and dataset provide a detailed localized breakdown of innovation within the state of Wyoming (WY) for the 2025 fiscal year. The chart ranks Wyoming cities and metropolitan areas by their volume of successfully granted patents, mapping out the core regional centers of technology, engineering, and research.Key Insights &amp; Data Points Included:<ul data-path-to-node="6,3"><li>Wyoming Innovation Clusters: Volume metrics tracking patent outputs in key WY hubs (such as Cheyenne, Laramie, Casper, and Jackson).</li><li>Regional R&amp;D Density: Insights into the local economic zones, university research footprints, and private entities driving intellectual property in the state.</li><li>2025 Patent Trends: Clean, structured data from 2025, perfect for analyzing regional technology growth and state-level competitive landscapes.</li><li>Legal &amp; Corporate Analytics: Ready-to-use dataset optimized for regional market research, corporate legal operations, and B2B competitive intelligence.</li></ul>Who is this for?
Ideal for legal tech analysts, intellectual property attorneys, economic development agencies, corporate R&amp;D strategists, and venture capital firms monitoring regional technology ecosystems across the United States.

Top cities by WY state in 2025

Top Cities by AK State in 2025: Alaska Patent &amp; Innovation HubsThis interactive visualization and dataset offer a localized breakdown of innovation activity across the state of Alaska (AK) for the 2025 fiscal year. The chart ranks Alaskan cities and research centers by their volume of granted patents, mapping out the state's regional technology hubs.Key Insights &amp; Data Points Included:<ul data-path-to-node="6,3"><li>Alaska Regional Tech Clusters: Volume metrics tracking patent filings in key AK urban and research centers (including Anchorage, Fairbanks, and Juneau).</li><li>Specialized Field Activity: Insights into patent density within cold-climate engineering, maritime technology (the Blue Economy), natural resources, and energy innovation.</li><li>Academic &amp; Private R&amp;D Footprint: Analysis of research outputs driven by state institutions (like the University of Alaska) and local commercial enterprises.</li><li>Structured Regional Data: Clean, 2025 baseline dataset ready for regional economic research, legal operations, and B2B competitive intelligence.</li></ul>Who is this for?
Ideal for legal tech analysts, IP attorneys, regional economic development offices, corporate R&amp;D strategists, and venture capital firms monitoring unique geographic and environmental technology developments in the United States.

Top cities by AK state in 2025

Top Cities by MT State in 2025: Montana Patent &amp; Innovation HubsThis interactive visualization and dataset deliver a comprehensive breakdown of technological leadership and R&amp;D output across Montana (MT) for the 2025 fiscal year. The chart ranks Montana cities, university research centers, and optics innovation clusters by their volume of granted utility patents, mapping out the state's national leadership in photonics, optical remote sensing (LiDAR), quantum computing components, biofilm engineering, pharmaceutical chemistry, advanced metallurgy, and environmental monitoring devices.Key Insights &amp; Data Points Included:<ul data-path-to-node="6,3"><li>Montana High-Tech &amp; Photonics Metros: Volume metrics tracking key patent-generating cities and research corridors across the state (including Bozeman, Missoula, Butte, Billings, Kalispell, Whitefish, and Helena).</li><li>High-Growth R&amp;D Sectors: Deep examination of patent concentration across integrated photonic circuits, radiation-tolerant computing hardware, industrial laser systems, microbial biofilm control, natural pharmaceutical compounds, and mineral extraction technology.</li><li>Enterprise, Federal &amp; Academic Synergy: Innovation metrics combining specialized regional technology companies (Bridger Photonics, Montana Instruments, Resonon, S2 Corporation, Revibro Optics) with top research anchors (Montana State University - MSU, Optical Technology Center - OpTeC, University of Montana - UM, Montana Technological University, and the EDA-designated Headwaters Tech Hub).</li><li>B2B &amp; Legal Tech Ready: Structured 2025 regional baseline dataset engineered for corporate site selection, Photonics/Optics supply chain benchmarking, venture capital due diligence, and university tech-transfer intelligence.</li></ul>Who is this for?
Essential for IP attorneys, optics &amp; quantum R&amp;D strategists, bio-health venture capital investors, tech transfer executives, and economic development boards tracking regional innovation trends across Montana and the Intermountain West tech ecosystem.

Top cities by MT state in 2025

Top Cities by ND State in 2025: North Dakota Patent &amp; Innovation HubsThis interactive visualization and dataset deliver a comprehensive breakdown of technological leadership and R&amp;D output across North Dakota (ND) for the 2025 fiscal year. The chart ranks North Dakota cities, university research foundations, and autonomous tech parks by their volume of granted utility patents, mapping out the state's national leadership in unmanned aerial systems (UAS/drones), autonomous agricultural machinery, polymer and bio-based coatings, energy extraction technologies, aerospace software, and precision diagnostic platforms.Key Insights &amp; Data Points Included:<ul data-path-to-node="6,3"><li>North Dakota High-Tech &amp; Autonomous Metros: Volume metrics tracking key patent-generating cities and research corridors across the state (including Fargo, Grand Forks, Bismarck, Minot, Williston, and Dickinson).</li><li>High-Growth R&amp;D Sectors: Deep examination of patent concentration across beyond-visual-line-of-sight (BVLOS) drone navigation systems, autonomous farming equipment, bio-based polymers and coatings, oil and gas extraction devices, and enterprise SaaS solutions.</li><li>Enterprise, Federal &amp; Academic Synergy: Innovation metrics combining tech industry anchors (Microsoft, John Deere Electronic Solutions, Bobcat Company, Northrop Grumman) with premier research hubs (North Dakota State University - NDSU, University of North Dakota - UND, Grand Sky Autonomous Systems Park, and Northern Plains UAS Test Site).</li><li>B2B &amp; Legal Tech Ready: Structured 2025 regional baseline dataset engineered for corporate site selection, AgTech and autonomous systems supply chain benchmarking, venture capital due diligence, and university tech-transfer intelligence.</li></ul>Who is this for?
Essential for IP attorneys, AgTech &amp; aerospace R&amp;D strategists, autonomous systems venture capital investors, tech transfer executives, and economic development boards tracking regional innovation trends across North Dakota and the upper Midwest tech ecosystem.

Top cities by ND state in 2025

Top Cities by SD State in 2025: South Dakota Patent &amp; Innovation HubsThis interactive visualization and dataset deliver a comprehensive breakdown of technological leadership and R&amp;D output across South Dakota (SD) for the 2025 fiscal year. The chart ranks South Dakota cities, university research parks, and biotechnology centers by their volume of granted utility patents, mapping out the state's national leadership in bio-ethanol refining, precision agricultural guidance, cyber-defense software, advanced metallurgy, digital display systems, and clinical healthcare technology.Key Insights &amp; Data Points Included:<ul data-path-to-node="6,3"><li>South Dakota High-Tech &amp; Agricultural Metros: Volume metrics tracking key patent-generating cities and university innovation corridors across the state (including Sioux Falls, Rapid City, Brookings, Madison, Vermillion, Yankton, and Aberdeen).</li><li>High-Growth R&amp;D Sectors: Deep examination of patent concentration across biofuel processing chemistry, autonomous farm equipment systems, threat-analysis software, high-performance alloys and critical minerals, Optoelectronic displays, and clinical medical devices.</li><li>Enterprise, Federal &amp; Academic Synergy: Innovation metrics combining corporate technology anchors (POET, Raven Industries, Daktronics, Sanford Health, Avera Health) with top higher-education research hubs (South Dakota State University - SDSU, South Dakota School of Mines and Technology - SDSMT, Dakota State University - DSU, University of South Dakota - USD, and the USD Discovery District).</li><li>B2B &amp; Legal Tech Ready: Structured 2025 regional baseline dataset engineered for corporate site selection, CleanTech and AgTech supply chain benchmarking, venture capital due diligence, and university tech-transfer intelligence.</li></ul>Who is this for?
Essential for IP attorneys, AgTech &amp; renewable energy R&amp;D strategists, materials science venture capital investors, tech transfer executives, and economic development boards tracking regional innovation trends across South Dakota and the Upper Midwest tech ecosystem.

Top cities by SD state in 2025

Average Weekly Injury Report Volume by Team

How many players does a team typically have on the injury report during any given week of the season? This insight calculates the average injury report size for a specific team in a specific week, built from 10 years of verified practice participation data across every team in the league. 
Injury volume isn't random — it tends to build as the season wears on, and some teams consistently carry heavier injury reports than others. Select a team and a week to see the real historical average, not a guess. Whether you're gauging a team's depth-chart risk heading into a specific week, sizing up an opponent's health before a matchup, or just tracking how injury-prone a roster has been over time, this gives you the number behind the trend. Methodology developed and reviewed by Dr. Kyle Trimble PT, DPT.

How often does a player actually suit up the week after first appearing on the injury report? This insight tracks every documented injury occurrence across 10 years of practice participation data and calculates the exact percentage of players who returned to play the very next week — no vague "questionable" guesswork, just the historical track record. Knowing who may play based on specific injury, position, and for particular team really allows you to look into the future, allowing you to make moves before the injury report comes out. 
Injury timelines vary widely, and a single week of DNP or Limited status doesn't always tell you what happens next. This tool cuts through that ambiguity with one verified number: the real return rate, built from official practice participation records spanning all 32 teams. Whether you're setting lineups, evaluating IR stashes, or projecting a multi-week absence, know the data before you commit. Methodology developed and reviewed by Dr. Kyle Trimble PT, DPT.

Return-to-Play Rate After 1 Week

Go beyond the standard Wednesday-Thursday-Friday injury report and see the complete picture. This tool tracks practice participation over 10 years of injury data to give you the likelihood of a player playing based on whether they are a full participant, limited, or didn't participate. How teams manage participation over the years during the practice week varies greatly across the league and knowing what patterns lead to a player playing can help you stay ahead of the curve.&nbsp; 
When performing your query, practice day 3 is the first practice day of the week, followed by practice day 2, followed by practice day 1. Each query runs off of a full three practice day set rather than basing probability off of one day of practice.&nbsp; <span style="font-size:12pt;font-family:Arial,sans-serif;color:#000000;background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">Methodology developed and reviewed by Dr. Kyle Trimble PT, DPT.<span style="font-size:11pt;font-family:Arial,sans-serif;color:#000000;background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">&nbsp;

Practice Pattern Probability

Track exactly how many times a specific injury has hit a specific team, broken out year by year — built from 10 years of practice participation data so you can spot whether a hamstring problem is a one-off or a pattern. Filter by team and injury type to see the trend line: is this injury climbing, fading, or holding steady across seasons? Perfect for spotting a team's recurring weak spot before it shows up on next season's injury report.
 <span style="font-size:12pt;font-family:Arial,sans-serif;color:#000000;background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">Methodology developed and reviewed by Dr. Kyle Trimble PT, DPT.<span style="font-size:11pt;font-family:Arial,sans-serif;color:#000000;background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">&nbsp;

Total Injury Instances by Team

4,142 fast-rising U.S. Google search terms (2020–2025) across 24 industries — each with a monthly interest time series, growth strength rating, search intent, and a plain-English explanation of what it is.

Fast Growing Search Trends 2026 is a curated dataset of 4,142 U.S. Google search terms whose popularity rose steadily from January 2020 through December 2025. These are durable, growing demand signals — not one-off viral spikes — selected for consistent upward trajectory over six years.
Every keyword is human-readable and enriched so you can act on it without extra cleanup:
<ul>
 <li>Trending Theme — the search term itself.</li>
 <li>What It Is About — a one-sentence plain-English explanation of the query, so non-experts understand each term instantly.</li>
 <li>Industry &amp; Sub-category — mapped to 24 industries (Shopping, Computers &amp; Electronics, Food &amp; Drink, Travel, Finance, Autos, Beauty &amp; Fitness, and more) plus a granular secondary category.</li>
 <li>Search Intent — commercial, informational, or navigational.</li>
 <li>Entity Type — brand, person, place, or other.</li>
 <li>Strength — a High / Medium / Low rating of how strong and consistent the growth trend is.</li>
 <li>Monthly Search Interest Index — 72 monthly values (Jan 2020 → Dec 2025) on Google's 0–100 relative scale, ready to chart as a time series.</li>
 <li>Live URL — a direct Google Trends link to inspect each term.</li>
</ul>
Coverage: United States · monthly · January 2020 – December 2025 · 24 industry verticals.
Ideal for market researchers, brand and category managers, investors, and content/SEO teams looking to spot emerging consumer demand early, size a category's momentum, or benchmark a brand against a rising theme.
Methodology note: Google Trends reports a 0–100 index normalized to each keyword's own peak month. Values describe a term's shape over time; they are not comparable as absolute search volume across terms.

Fast Growing Search Trends by Industry

TxODDS- Odds Updates for 2026 World Cup Knockout Stage

World Cup 2026 Knockout Fixtures

2,361 fast-rising U.S. health search terms (2020–2026), each with a monthly interest time series, growth-strength score, patient-journey stage, search intent, brand signal, and the industry buyers it matters to.

Health Search Trends 2026 is a curated dataset of 2,361 U.S. health-related Google search terms whose popularity rose steadily and consistently from January 2020 through December 2025. Every term passed a statistical filter for durable growth (positive linear slope and R-squared of at least 0.50) — these are structural shifts in health demand, not one-off spikes.
Each keyword is enriched well beyond a raw trend line, so buyers can segment by commercial and patient-journey meaning without extra modeling:
<ul>
 <li>Growth strength — a 0–100 trend-strength score combining how fast a term rose with how consistently it rose, plus a Strong / Solid / Moderate tier. The underlying slope and R-squared are the selection filter described above; the score is what ships, because it is the form you can rank and segment on directly.</li>
 <li>Topical category — one of 22 clusters, including a small Pet &amp; Animal Health group (17 terms, also flagged by <code>is_veterinary</code>) and an Other / Miscellaneous residual (16 terms), so you can exclude both (GLP-1 &amp; weight, gut health, cholesterol, thyroid, autoimmune skin, mental health, and more).</li>
 <li>Journey stage — where the search sits in the patient journey (symptom noticing, condition understanding, diagnosis seeking, treatment discovery, ongoing management, and others).</li>
 <li>Intent type — what the searcher wants (informational, product discovery, comparison, diagnostic-interpretive, access/affordability, and more).</li>
 <li>Brand signal — whether a named brand is present and its commercial relationship (branded drug, unbranded disease, unbranded drug class, and others).</li>
 <li>Buyer relevance — which industry segments the signal matters to (pharma brand, provider/HCO, consumer health, retail pharmacy, payer/insurance, investor).</li>
 <li>Commercial flags — generic drug name present, comparison query, or a cost/access/insurance signal.</li>
 <li>Monthly Search Interest Index — 72 monthly values (Jan 2020 → Dec 2025) on Google's 0–100 relative scale, ready to chart.</li>
</ul>
Coverage: United States · monthly · January 2020 – December 2025 · 21 health categories · 2,361 keywords.
Ideal for pharma brand and market-access teams, consumer-health and OTC marketers, health-tech and digital-health startups, payers, providers, and healthcare investors who need to spot rising conditions, treatments, and branded demand early.
Methodology note: Google Trends reports a 0–100 index normalized to each keyword's own peak month. Values describe a term's shape over time; they are not comparable as absolute search volume across terms. The five annotation fields were produced by an LLM against a fixed schema and reflect a single best-fit label per field.

Health Search Trends 2026  USA

Every injury carries a cost. This data product quantifies exactly what that cost is — the average number of games a player misses for any given injury type, broken down by position, and specific to each team, giving you a precise, data-driven baseline for evaluating roster risk, trade value, and lineup decisions before the rest of the market has caught up. 
Built from a comprehensive NFL injury dataset spanning&nbsp; across all 32 teams over 11 years, this product calculates average games missed at the player-season level across every injury type in the dataset — from hamstrings and knees to concussions and shoulders. Knowing the historical average gives you a baseline that raw injury rotors simply can’t provide.&nbsp;
Search and filter by:
<ul class="[li_&amp;]:mb-0 [li_&amp;]:mt-1 [li_&amp;]:gap-1 [&amp;:not(:last-child)_ul]:pb-1 [&amp;:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3">
<li class="font-claude-response-body whitespace-normal break-words pl-2">Injury Type — look up any injury and instantly see how many games it costs a player on average across the entire NFL if they have a designation of Questionable-Out, Doubtful-Out, or Out.</li>
<li class="font-claude-response-body whitespace-normal break-words pl-2">Team — analyze franchise-level injury trends and see which organizations consistently lose more player-games to specific injury types</li>
</ul>
Don't just know a player is hurt. Know what it means. This product turns raw injury data into a number you can act on — giving you the historical context to make smarter decisions from the moment the injury report drops to the final injury designation on Sunday. If you get a message&nbsp;"No data found matching your criteria. Try adjusting your filters. You will not be charged." This likely means that the team did not have a statistically significant amount of players at that position with that injury.&nbsp; <span style="font-size:12pt;font-family:Arial,sans-serif;color:#000000;background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">Methodology developed and reviewed by Dr. Kyle Trimble PT, DPT.<span style="font-size:11pt;font-family:Arial,sans-serif;color:#000000;background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">&nbsp;

Team Games Missed Average

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Unlock one of the most actionable metrics in NFL injury analytics — the average number of weeks a player spends on the injury report for any given injury type. When a player is on the injury report, it means their availability may be in question or that they are receiving treatment. This also means that the injury is still affecting their participation and in turn, affecting their performance. Just because they have no designation doesn't mean they are 100 percent healthy. Understanding how long a player spends on the injury report also helps you cut through the noise when the injury report grows long late into the season.&nbsp;Whether you're managing a lineup, planning around an absence, or building a fantasy roster, knowing how long an injury can linger gives you an earlier read than the injury report alone. 
This data product pulls directly from a comprehensive NFL injury report dataset spanning 10 years, covering all 32 teams, and every position group. Search and filter by:
<ul class="[li_&amp;]:mb-0 [li_&amp;]:mt-1 [li_&amp;]:gap-1 [&amp;:not(:last-child)_ul]:pb-1 [&amp;:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3">
<li class="font-claude-response-body whitespace-normal break-words pl-2">Player — look up any individual player's injury report history and average time missed</li>
<li class="font-claude-response-body whitespace-normal break-words pl-2">Game Status — filter by Out, Questionable, Doubtful, or IR designations to understand severity</li>
<li class="font-claude-response-body whitespace-normal break-words pl-2">Position — compare how long quarterbacks, running backs, wide receivers, and other position groups typically miss</li>
<li class="font-claude-response-body whitespace-normal break-words pl-2">Team — analyze how specific franchises manage injured players and roster availability</li>
<li class="font-claude-response-body whitespace-normal break-words pl-2">Year — track trends over time and see how injury report durations have shifted season by season</li>
<li class="font-claude-response-body whitespace-normal break-words pl-2">Injury Type — drill into specific injuries like hamstring, knee, ankle, concussion, and more</li>
</ul>
From a DNP on Wednesday to a game-time decision on Sunday, this metric tells you exactly how long that window typically lasts — giving you the data to make smarter, faster decisions when it matters most. <span style="font-size:12pt;font-family:Arial,sans-serif;color:#000000;background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">Methodology developed and reviewed by Dr. Kyle Trimble PT, DPT.<span style="font-size:11pt;font-family:Arial,sans-serif;color:#000000;background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">&nbsp;

Time Spent on Injury Report

Ever want to pull all the injuries for a specific position over 10 years or see how often a particular injury occurs for a specific team? Now you can get the raw data all at once instead of combing through injury reports, spending hours of your own time. Injury reports tell a story and seeing the raw data behind a certain team or looking for a particular injury will save you time and ensure accuracy.&nbsp;<div> </div><div>Methodology developed and reviewed by Dr. Kyle Trimble PT, DPT.&nbsp;<div> </div></div>

Team Positional Injury Histories

Banged Up Bills

What is the percentage of players who clear concussion protocol to play within a week?

Players who return within a week from a concussion are one of the most valuable and actionable data points in the NFL — identifying who bounces back fast matters for fantast purposes, and availability often changes before the injury report reflects it.&nbsp;<div><div> </div><div>Our data isolates exactly which positions and player profiles are most likely to clear the concussion protocol within a week with each team, giving analysis a verified baseline in a landscape where everyone else is assuming the worst or guessing.&nbsp;</div><div> </div><div>Methodology developed and reviewed by Dr. Kyle Trimble PT, DPT.&nbsp;<div> </div></div></div>

Concussion Play Percentage

Identify a team's questionable injury rate who played by given year

A team's questionable tag is one of the measurable differences between teams in the NFL, some organizations consistently suit up their questionable players while others use it as a smokescreen, and knowing the difference can help you navigate uncertainty.&nbsp;<div> </div><div>Our query tool lets you pull the exact rate at which any team's questionable players actually take the field, giving you a data-backed read on how much to trust a team's injury designations week after week. Look at the breakdown by team, year, and even position which gives you answers that simply didn’t&nbsp;exist before.&nbsp;<div> </div><div>Methodology developed and reviewed by Dr. Kyle Trimble PT, DPT.&nbsp;<div> </div></div></div>

Team Questionable Rates By Year

Average time missed for a groin strain in the NFL

Groin strains are one of the NFL's most underrated cause of missed time — quietly costing teams key contributors for weeks at a time. Our data tracks the exact average missed game time for groin strains across every NFL position, giving you a verified baseline when evaluating injury reports and making start/sit decisions.&nbsp; Methodology developed and reviewed by Dr. Kyle Trimble PT, DPT.

Average Missed Time Groin Injury

Spartera powers zero-data-movement analytics, delivering on-demand insights from verified data sources via a single API call.

Counts the occurrences of different play types (e.g., pass, rush) to understand offensive tendencies.

Total Plays by Play Type

Measures the average EPA for plays that result in a touchdown, broken down by play type (run, pass), to assess the efficiency of scoring plays.

Average EPA for Plays Resulting in a Touchdown by Play Type

Analyze arena capacities to understand team's potential revenue and fan base.

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