Cleveland Browns vs Chicago Bears Match Player Stats
The Cleveland Browns vs Chicago Bears Match Player Stats from the August 15, 2026, NFL preseason game reveal how Chicago turned a competitive opening quarter into a convincing 34-10 victory. The Bears finished with 34 points, while the Browns managed 10, as Chicago’s passing efficiency, defensive takeaways, and second-half scoring created the decisive difference. For fans who want more than the final score, the individual numbers explain which quarterbacks moved the ball, which receivers made the biggest plays, and why Cleveland struggled to keep pace.
The preseason matchup offered an opportunity to examine player performances across passing, rushing, receiving, defense, and special teams. Chicago recorded 348 passing yards and three passing touchdowns, while Cleveland finished with 206 passing yards and no passing touchdowns. The Browns did find success on the ground, producing 108 rushing yards compared with Chicago’s 82, but that advantage did not translate into enough points.
One important distinction matters when interpreting these numbers: this was a preseason game, not a regular-season contest. Player rotations, evaluation priorities, and opportunities for developing players can differ significantly from those in competitive games later in the year. The statistics therefore provide useful evidence about what happened on the field, but they should not be treated as a complete prediction of either team’s regular-season performance.
Cleveland Browns vs Chicago Bears Match Player Stats: Game Overview
The Chicago Bears defeated the Cleveland Browns 34-10 on August 15, 2026. Chicago scored 10 points in the second quarter, added 14 in the third, and finished with another 10 in the fourth. Cleveland scored three points in the opening quarter and seven in the second before being held scoreless throughout the second half.
Final Score and Quarter-by-Quarter Results
| Quarter | Cleveland Browns | Chicago Bears |
| First quarter | 3 | 0 |
| Second quarter | 7 | 10 |
| Third quarter | 0 | 14 |
| Fourth quarter | 0 | 10 |
| Final score | 10 | 34 |
The score progression tells a clear story. Cleveland started with a narrow advantage, but Chicago responded before halftime and then established complete control after the break.
The third quarter was particularly important. Chicago scored 14 unanswered points during that period, turning a relatively close contest into a much more comfortable lead. The Bears added another 10 points in the fourth quarter, while Cleveland could not find a scoring response.
Team Statistics at a Glance
| Statistical Category | Cleveland Browns | Chicago Bears |
| Total passing yards | 206 | 348 |
| Passing touchdowns | 0 | 3 |
| Passing interceptions thrown | 2 | 1 |
| Rushing attempts | 27 | 21 |
| Rushing yards | 108 | 82 |
| Rushing touchdowns | 1 | 1 |
| Receptions | 22 | 25 |
| Receiving yards | 228 | 368 |
| Total sacks recorded by defense | 2 | 3 |
| Interceptions recorded by defense | 1 | 2 |
| Field goals made | 1 | 2 |
| Final points | 10 | 34 |
The comparison highlights an important feature of the game: Cleveland gained more rushing yards, but Chicago was substantially more productive through the air.
The Bears recorded 142 more passing yards than the Browns. They also threw three touchdown passes, compared with none for Cleveland. That difference in passing production was a major factor in the final result.
Chicago also recorded two interceptions and three sacks on defense. Cleveland’s defense managed one interception and two sacks, but the Browns could not generate enough offensive production to offset Chicago’s scoring advantage.
Quarterback Performance: Who Delivered the Best Numbers?
Quarterback statistics offer one of the clearest explanations for the result. Cleveland used three quarterbacks, while Chicago also distributed passing opportunities among three players. Their completion rates, passing yardage, touchdown production, and turnovers show why the teams finished with such different offensive totals.
Deshaun Watson Player Stats
Deshaun Watson led Cleveland’s listed passers in passing yardage and posted the strongest passer rating among the Browns’ three quarterbacks.
| Statistic | Deshaun Watson |
| Completions | 11 |
| Pass attempts | 15 |
| Completion percentage | 73.3% |
| Passing yards | 126 |
| Yards per attempt | 8.4 |
| Passing touchdowns | 0 |
| Interceptions thrown | 0 |
| Sacks taken | 1 |
| Sack yardage | 5 |
| Passer rating | 98.2 |
Watson completed 11 of 15 passes, giving him a completion rate of 73.3%. His 126 passing yards represented more than half of Cleveland’s total passing production.
The 8.4 yards per attempt figure is also worth noting. It suggests that Watson generated useful yardage from his passing opportunities rather than relying entirely on short completions. However, his statistical line did not include a touchdown pass, so the yardage did not produce the same scoring impact seen from Chicago’s quarterbacks.
Watson took one sack for five yards. His performance was relatively efficient by completion rate and yards per attempt, but the broader team result shows why quarterback evaluation should include scoring and turnover context alongside passing accuracy.
A strong completion percentage can indicate good ball placement and decision-making, but it does not automatically mean an offense is finishing drives successfully. In this game, Cleveland’s passing attack produced yards without recording a passing touchdown.
Shedeur Sanders Player Stats
Shedeur Sanders completed six passes for 79 yards, but an interception reduced the effectiveness of his overall statistical line.
| Statistic | Shedeur Sanders |
| Completions | 6 |
| Pass attempts | 11 |
| Completion percentage | 54.5% |
| Passing yards | 79 |
| Yards per attempt | 7.2 |
| Passing touchdowns | 0 |
| Interceptions thrown | 1 |
| Sacks taken | 0 |
| Passer rating | 39.6 |
Sanders completed slightly more than half of his attempts. His 79 passing yards and 7.2 yards per attempt show that he generated some downfield production, but the interception was an important negative in a game Cleveland ultimately lost by 24 points.
The difference between Sanders’ 7.2 yards per attempt and his 39.6 passer rating illustrates why individual statistics must be read together. Yardage per attempt measures passing output relative to attempts, while passer rating incorporates several elements of quarterback performance, including completions, yards, touchdowns, and interceptions.
For preseason evaluation, the useful questions go beyond whether a quarterback gained yards. Did he protect the football? Did he sustain drives? Did he make efficient decisions under pressure? The available statistics provide part of the answer, although they do not establish the full circumstances surrounding each play.
Dillon Gabriel Player Stats
Dillon Gabriel completed five passes for 23 yards and threw one interception. He also took two sacks for a combined loss of 17 yards.
| Statistic | Dillon Gabriel |
| Completions | 5 |
| Pass attempts | 8 |
| Completion percentage | 62.5% |
| Passing yards | 23 |
| Yards per attempt | 2.9 |
| Passing touchdowns | 0 |
| Interceptions thrown | 1 |
| Sacks taken | 2 |
| Sack yardage | 17 |
| Passer rating | 27.1 |
Gabriel’s completion percentage was higher than Sanders’, but his overall passing production was lower. He averaged just 2.9 passing yards per attempt and lost 17 yards on sacks.
Those sack losses matter because passing efficiency is not simply about the number of completed throws. Quarterbacks must also avoid plays that move the offense backward and put it in difficult down-and-distance situations.
Gabriel’s statistical line also included an interception and no passing touchdowns. The combination of limited yardage, sacks, and a turnover made it difficult for Cleveland’s offense to build momentum during his recorded opportunities.
Tyson Bagent Player Stats
Tyson Bagent led Chicago’s listed passers in total passing attempts and completions. He threw one touchdown and one interception.
| Statistic | Tyson Bagent |
| Completions | 13 |
| Pass attempts | 22 |
| Completion percentage | 59.1% |
| Passing yards | 169 |
| Yards per attempt | 7.7 |
| Passing touchdowns | 1 |
| Interceptions thrown | 1 |
| Sacks taken | 2 |
| Sack yardage | 20 |
| Passer rating | 79.5 |
Bagent completed 13 of 22 attempts for 169 yards. His 7.7 yards per attempt indicates that he generated a useful amount of passing production despite taking two sacks.
He also recorded a touchdown pass, giving Chicago a scoring result that Cleveland’s quarterbacks did not match. His interception shows that the Bears’ passing attack was not mistake-free, but Chicago still finished the game with three passing touchdowns.
Bagent’s numbers provide a useful example of why quarterback performance should be judged in context. His completion percentage was lower than Watson’s, yet his touchdown production contributed directly to Chicago’s stronger offensive result.
Case Keenum Player Stats
Case Keenum produced Chicago’s most efficient passing line among the three listed quarterbacks.
| Statistic | Case Keenum |
| Completions | 9 |
| Pass attempts | 10 |
| Completion percentage | 90.0% |
| Passing yards | 151 |
| Yards per attempt | 15.1 |
| Passing touchdowns | 2 |
| Interceptions thrown | 0 |
| Sacks taken | 0 |
| Passer rating | 158.3 |
Keenum completed nine of his 10 attempts, a 90% completion rate. More significantly, he generated 151 passing yards and two touchdown passes without throwing an interception or taking a sack.
His 15.1 yards per attempt was the highest among all six quarterbacks listed in the game statistics. That figure indicates that Chicago obtained substantial yardage from each passing opportunity he received.
Keenum’s passer rating of 158.3 was also the highest among the listed quarterbacks. His performance combined accuracy, explosive yardage production, touchdown passes, and ball security.
The most revealing comparison is between Keenum and Cleveland’s passers. Keenum’s 151 passing yards were greater than the totals recorded by Sanders and Gabriel individually, and he threw two touchdowns without an interception. Those are strong results in a limited preseason sample, although they should not be treated as proof of future regular-season success.
Miller Moss Player Stats
Miller Moss completed three of eight passes for 48 yards. He did not throw a touchdown or interception and was not sacked.
| Statistic | Miller Moss |
| Completions | 3 |
| Pass attempts | 8 |
| Completion percentage | 37.5% |
| Passing yards | 48 |
| Yards per attempt | 6.0 |
| Passing touchdowns | 0 |
| Interceptions thrown | 0 |
| Sacks taken | 0 |
| Passer rating | 58.3 |
Moss had fewer completed passes than either of Chicago’s other listed quarterbacks. His 48 passing yards offered some production, but a completion rate of 37.5% limited his overall passing output.
He avoided interceptions and sacks, which helped protect the ball, but he did not record a touchdown. His statistics therefore present a mixed picture rather than a clear indication of either success or failure.
Preseason passing numbers need to be considered alongside the number of opportunities a player receives. Eight attempts are a relatively small sample, so the results are best understood as one part of a broader evaluation.
Quarterback Comparison: The Most Important Differences
Across all three listed passers, Chicago recorded 25 completions on 40 attempts for 348 yards, three touchdowns, and one interception. Cleveland recorded 22 completions on 34 attempts for 206 yards, no touchdowns, and two interceptions.
The differences are substantial:
- Passing yardage: Chicago led by 142 yards.
- Passing touchdowns: Chicago recorded three more.
- Interceptions thrown: Cleveland threw two, compared with one for Chicago.
- Team passer rating: Chicago finished at 107.1, while Cleveland recorded 59.4.
- Sacks taken: Cleveland’s quarterbacks took three sacks for 22 yards, while Chicago’s quarterbacks took two for 20 yards.
The central lesson is that passing production must create scoring opportunities to have its full effect on the scoreboard. Cleveland’s quarterbacks generated some useful individual numbers, particularly Watson’s completion rate, but Chicago combined yardage with touchdowns more effectively.
Running Back Statistics: Cleveland’s Ground Game vs Chicago’s
The rushing numbers reveal a different side of the matchup. Cleveland gained more rushing yards than Chicago, yet the Browns still lost by 24 points. This contrast is important because it shows that winning one statistical category does not necessarily mean winning the game.
Cleveland Browns Rushing Leaders
| Player | Carries | Rushing Yards | Average | Longest Run | TDs |
| Davon Booth | 4 | 53 | 13.3 | 28 | 0 |
| KC Concepcion | 1 | 14 | 14.0 | 14 | 0 |
| Raheim Sanders | 2 | 9 | 4.5 | 5 | 0 |
| Quinshon Judkins | 4 | 9 | 2.3 | 6 | 0 |
| Dillon Gabriel | 1 | 6 | 6.0 | 6 | 0 |
| Dylan Sampson | 4 | 6 | 1.5 | 4 | 0 |
| Deshaun Watson | 3 | 4 | 1.3 | 2 | 0 |
| Ahmani Marshall | 6 | 4 | 0.7 | 6 | 0 |
| Shedeur Sanders | 2 | 3 | 1.5 | 3 | 0 |
| Team total | 27 | 108 | 4.0 | 28 | 1 |
Davon Booth’s Explosive Rushing Performance
Davon Booth was Cleveland’s leading listed rusher, gaining 53 yards on four carries. His average of 13.3 yards per attempt was the highest among the Browns’ players with multiple carries, and his longest run covered 28 yards.
Booth’s contribution deserves attention because he produced almost half of Cleveland’s total rushing yardage on only four attempts. His numbers demonstrate the impact a single explosive runner can have on a team’s aggregate rushing statistics.
However, four carries are not enough to establish that a player can produce the same efficiency consistently. A long run can raise an average significantly, especially when the total number of attempts is small. Evaluators would need more evidence about consistency, blocking assignments, ball security, and performance in short-yardage situations before drawing broader conclusions.
For readers analyzing individual player production, Booth’s performance is a reminder to examine both total yardage and workload. Fifty-three yards on four carries is an impressive statistical contribution, but the sample remains limited.
KC Concepcion’s Versatility
KC Concepcion recorded 14 rushing yards on one carry and also caught three passes for 27 yards. His rushing attempt resulted in a 14-yard gain, and his receiving contribution added another dimension to Cleveland’s offense.
Combining his rushing and receiving numbers, Concepcion produced 41 yards from scrimmage on the listed statistics. He also returned one punt for 31 yards.
These numbers demonstrate production in three distinct areas: rushing, receiving, and punt returns. That versatility can be useful when assessing a player’s potential contribution to different offensive and special-teams situations.
His statistics should not, however, be treated as proof of a particular future role. A single preseason game can reveal the ways a player contributed on that day, but determining long-term responsibilities requires more evidence.
Quinshon Judkins and Dylan Sampson
Quinshon Judkins carried the ball four times for nine yards, averaging 2.3 yards per attempt. Dylan Sampson also received four rushing attempts, finishing with six yards at an average of 1.5 yards per carry.
Their rushing totals were modest compared with Booth’s output. Still, raw rushing averages alone cannot explain why a running play succeeds or fails. Defensive alignment, blocking execution, down and distance, and the specific situation all affect the outcome.
Judkins’ and Sampson’s numbers show that Cleveland’s rushing production was concentrated among a small number of players rather than evenly distributed across the backfield.
A more complete assessment would examine whether their carries came in short-yardage situations, against particular defensive personnel, or during stretches when Cleveland was prioritizing evaluation over immediate scoring. The available statistics do not establish those details, so they should not be assumed.
Chicago Bears Rushing Leaders
| Player | Carries | Rushing Yards | Average | Longest Run | TDs |
| Roschon Johnson | 8 | 34 | 4.3 | 19 | 0 |
| Salvon Ahmed | 2 | 17 | 8.5 | 12 | 0 |
| Coleman Bennett | 4 | 10 | 2.5 | 8 | 1 |
| Brittain Brown | 5 | 8 | 1.6 | 6 | 0 |
| Tyson Bagent | 1 | 7 | 7.0 | 7 | 0 |
| Case Keenum | 1 | 6 | 6.0 | 6 | 0 |
| Team total | 21 | 82 | 3.9 | 19 | 1 |
Roschon Johnson led Chicago in rushing attempts and rushing yards, finishing with 34 yards on eight carries. His workload was twice Booth’s, although his average gain was much lower.
Salvon Ahmed recorded 17 yards on two carries, giving him an 8.5-yard average. His rushing contribution was complemented by three receptions for 55 yards and a receiving touchdown.
Coleman Bennett scored a rushing touchdown on four carries for 10 yards. His scoring contribution illustrates an important distinction between rushing volume and scoring value: a player can finish with a modest yardage total while still making a direct contribution to the final score.
Chicago’s overall rushing production was less explosive than Cleveland’s in terms of total yards, but it was sufficient to complement the Bears’ much stronger passing attack.
Why Cleveland Won the Rushing Battle but Lost the Game
Cleveland gained 108 rushing yards on 27 attempts, while Chicago gained 82 yards on 21 attempts. The Browns therefore held a 26-yard advantage in rushing yardage.
Yet Chicago won 34-10.
The explanation lies in the combination of passing production, touchdowns, and turnovers. Chicago generated 348 passing yards and three passing touchdowns. Cleveland recorded 206 passing yards and no passing touchdowns.
The Bears also returned two interceptions for a combined 55 yards, while Cleveland’s defense recorded one interception for two yards. Those defensive plays gave Chicago additional opportunities to influence field position and possession outcomes.
The broader takeaway is straightforward: rushing efficiency is only one component of offensive performance. Teams must also turn possessions into points, avoid costly mistakes, and generate enough production through the passing game when the situation demands it.
Receiving Statistics: Which Players Made the Biggest Impact?
Receiving statistics help identify who converted quarterback opportunities into meaningful yardage. They also reveal the difference between accumulating catches and creating explosive plays.
The listed team receiving totals show that Cleveland recorded 228 receiving yards on 22 receptions, while Chicago recorded 368 yards on 25 receptions. Chicago gained 140 more receiving yards despite catching only three additional passes.
Cleveland Browns Receiving Leaders
| Player | Receptions | Receiving Yards | Average | Longest Reception | Targets |
| Dylan Sampson | 2 | 43 | 21.5 | 43 | 2 |
| Gage Larvadain | 4 | 37 | 9.3 | 24 | 7 |
| Luke Floriea | 1 | 35 | 35.0 | 35 | 1 |
| KC Concepcion | 3 | 27 | 9.0 | 11 | 2 |
| Ahmani Marshall | 3 | 19 | 6.3 | 9 | 4 |
| Jerry Jeudy | 2 | 18 | 9.0 | 14 | 4 |
| Denzel Boston | 1 | 15 | 15.0 | 15 | 2 |
| Isaiah Bond | 1 | 8 | 8.0 | 8 | 2 |
| Kole Wilson | 1 | 7 | 7.0 | 7 | 1 |
| Raheim Sanders | 2 | 7 | 3.5 | 5 | 2 |
| Blake Whiteheart | 1 | 6 | 6.0 | 6 | 1 |
| Carsen Ryan | 1 | 6 | 6.0 | 6 | 2 |
Dylan Sampson’s Receiving Contribution
Dylan Sampson led Cleveland’s listed receivers in receiving yards, gaining 43 yards on two receptions. His average of 21.5 yards per catch indicates that both completed passes produced substantial yardage.
Sampson also recorded four rushing attempts for six yards. Combining his rushing and receiving totals gives him 49 yards from scrimmage in the listed statistics.
That combined figure offers a more complete view of his contribution than receiving yardage alone. For players who contribute in multiple phases of the offense, evaluating only one statistical category can leave out meaningful production.
Still, his 43 receiving yards came on only two catches. That is a useful indication of explosive output in this game, but a larger sample would be needed to assess how reliably he can generate those gains.
Gage Larvadain’s Target Volume
Gage Larvadain caught four passes for 37 yards on seven targets. His four receptions were the highest total among Cleveland’s listed receivers, while his target total was also the largest.
His 57.1% catch rate, calculated from four receptions on seven targets, provides additional context. He received more passing opportunities than any other listed Cleveland receiver but converted a little over half of those targets into catches.
Target volume can be useful when assessing involvement in an offense, but it should not be confused with receiving efficiency. A target can be incomplete for several reasons, and the summary statistics alone do not reveal whether the cause was a difficult throw, tight coverage, a drop, or another factor.
Larvadain’s numbers therefore suggest meaningful involvement, while the limited game data leaves the details of individual targets unresolved.
Luke Floriea’s One-Catch, 35-Yard Result
Luke Floriea caught one pass for 35 yards on one target. His 35.0-yard average was the highest among Cleveland’s listed receivers.
This is a strong example of how a single explosive reception can make a player’s statistical line stand out. Floriea generated 35 yards without requiring multiple receptions, helping Cleveland’s total receiving production.
However, a one-target performance should be interpreted carefully. His catch rate was 100% in this sample, but that does not establish that he will maintain the same rate over a larger workload. It simply means he caught the only pass directed to him in the listed statistics.
KC Concepcion and Jerry Jeudy
Concepcion caught three passes for 27 yards on two listed targets, while Jeudy caught two passes for 18 yards on four targets.
These entries contain a data consistency issue that deserves attention. Concepcion’s listed receptions exceed his targets, which is not possible under the usual definition of a receiving target. As a result, his reported target figure should be verified against the original box score before using it to calculate a catch rate.
This is exactly why responsible sports-statistics analysis requires more than copying numbers into a table. If a source contains an internally inconsistent entry, the discrepancy should be acknowledged rather than silently turned into a misleading performance metric.
Jeudy’s four targets and two catches, by contrast, yield a 50% catch rate. His 18 yards came on an average of nine yards per reception.
Chicago Bears Receiving Leaders
| Player | Receptions | Receiving Yards | Average | Longest Reception | TDs |
| Kaden Davis | 4 | 88 | 22.0 | 41 | 0 |
| Ray-Ray McCloud III | 3 | 63 | 21.0 | 28 | 0 |
| Salvon Ahmed | 3 | 55 | 18.3 | 49 | 1 |
| Maurice Alexander | 3 | 42 | 14.0 | 25 | 1 |
| Scotty Miller | 3 | 37 | 12.3 | 18 | 0 |
| JP Richardson | 2 | 34 | 17.0 | 18 | 0 |
| Sam Roush | 2 | 15 | 7.5 | 8 | 0 |
| Brittain Brown | 2 | 12 | 6.0 | 6 | 1 |
| Stephen Carlson | 1 | 11 | 11.0 | 11 | 0 |
| Qadir Ismail | 1 | 8 | 8.0 | 8 | 0 |
| Roschon Johnson | 1 | 3 | 3.0 | 3 | 0 |
Kaden Davis Leads Chicago in Receiving Yards
Kaden Davis finished with four receptions for 88 yards, the highest receiving total among the listed players on either team. His average of 22 yards per catch and longest reception of 41 yards demonstrate how much yardage he generated from a relatively small number of receptions.
Davis was a key statistical contributor to Chicago’s 368 receiving yards. His performance also illustrates why total catches are not enough to identify the most productive receiver. A player with four receptions can outgain a player with more catches if those receptions produce larger gains.
Davis did not record a receiving touchdown in the supplied statistics, so his contribution was primarily reflected in yardage rather than direct scoring.
Ray-Ray McCloud III Adds Explosive Production
Ray-Ray McCloud III caught three passes for 63 yards, averaging 21 yards per reception. His longest catch covered 28 yards.
That production placed him second among Chicago’s listed receivers in yardage. Along with Davis, he helped the Bears create substantial receiving output without relying on a large number of completed passes.
McCloud also returned one punt for 11 yards. His listed receiving and punt-return contributions show activity in more than one phase of the game, although the statistics alone do not establish his complete role within the team’s preseason evaluation.
Salvon Ahmed’s Dual-Threat Contribution
Salvon Ahmed recorded 55 receiving yards on three catches, including a 49-yard reception and a receiving touchdown. He also gained 17 rushing yards on two carries.
His combined rushing and receiving production was 72 yards from scrimmage, with one receiving touchdown. That total made him one of Chicago’s most productive all-around offensive contributors in the listed statistics.
Ahmed’s long reception was especially significant because it accounted for a large portion of his receiving yardage. His touchdown also converted that receiving production into a direct scoring result.
The combination of rushing involvement, receiving yardage, and a touchdown makes Ahmed’s performance more informative than a receiving-only comparison would suggest.
Maurice Alexander and the Value of Scoring Plays
Maurice Alexander caught three passes for 42 yards and one touchdown. He averaged 14 yards per reception, with a longest catch of 25 yards.
He also returned two kickoffs for 52 yards, averaging 26 yards per return, and recorded two punt returns for eight yards.
Alexander’s numbers show how a player can contribute through both offense and special teams. His receiving touchdown was a direct scoring contribution, while his return yardage added field-position value.
For a detailed player review, these different categories should be kept separate before being combined into an overall performance summary. Receiving yards, kickoff-return yards, and punt-return yards come from different situations and should not be treated as interchangeable measures of offensive production.
Why Chicago’s Receiving Numbers Were More Effective
Chicago finished with 368 receiving yards, compared with Cleveland’s 228. The Bears also recorded three receiving touchdowns, while Cleveland recorded none.
The Bears averaged 14.7 yards per reception, compared with Cleveland’s 10.4. That 4.3-yard difference per catch helps explain why Chicago generated more receiving yardage despite recording only three additional receptions.
The data points to a combination of explosive catches and touchdown production. Chicago did not simply catch more passes. It gained more yards from those catches and converted several passing opportunities into scores.
For readers comparing individual receivers, three measures are especially useful:
- Total receiving yards: Shows how much yardage a player produced.
- Yards per reception: Helps identify explosive or high-gain contributions.
- Touchdowns: Shows whether receiving production directly contributed to scoring.
Targets and catch rate can add context, but only when the underlying data is internally consistent.
Defensive Player Stats: Interceptions, Sacks, and Pressure
Offensive production explains much of Chicago’s advantage, but the defensive numbers reveal another important part of the game. The Bears recorded two interceptions and three sacks, while Cleveland managed one interception and two sacks.
These plays matter because defensive success can affect more than the immediate statistical result. An interception ends an opposing possession, while a sack can create a difficult down-and-distance situation and reduce the chance of a successful drive.
Team Defensive Comparison
| Defensive Statistic | Cleveland Browns | Chicago Bears |
| Total tackles | 61 | 76 |
| Solo tackles | 44 | 43 |
| Sacks | 2 | 3 |
| Tackles for loss | 5 | 7 |
| Passes defended | 9 | 8 |
| Quarterback hits | 5 | 5 |
| Interceptions | 1 | 2 |
| Interception return yards | 2 | 55 |
| Defensive touchdowns | 0 | 0 |
Chicago recorded 76 total tackles, compared with Cleveland’s 61. The Bears also recorded more sacks and tackles for loss, while both teams registered five quarterback hits.
Cleveland did finish with one more pass defended than Chicago. That figure indicates the Browns had defensive plays that disrupted or challenged passes, even though Chicago produced more interceptions.
The statistics suggest that Chicago created more negative plays and generated more turnover opportunities. However, total tackles should not be used as a direct measure of defensive quality on their own. A defense can record many tackles because opponents complete numerous short passes, while another defense may force incompletions or turnovers that prevent tackles from occurring.
The most meaningful interpretation combines tackles with interceptions, sacks, tackles for loss, and the points allowed.
Beanie Bishop Jr. Records Two Interceptions
Beanie Bishop Jr. recorded two interceptions for Chicago, returning them for a combined 55 yards. His interceptions were the most significant individual defensive statistics listed for either team.
An interception can change the direction of a game by ending an opponent’s drive and giving the defending team possession. Return yardage can add further value by improving field position.
Bishop’s 55 return yards show that his interceptions were not merely recorded as turnovers without additional movement. They also generated measurable return production.
The supplied statistics do not establish the exact circumstances of each interception, so it would be inappropriate to claim that either came from a particular coverage, quarterback mistake, or tactical adjustment. What the numbers establish is that Chicago recorded two interceptions, and Bishop was credited with both.
Michael Coats Jr. Provides Cleveland’s Interception
Michael Coats Jr. recorded Cleveland’s only interception, returning it for two yards. The play gave the Browns a defensive takeaway, but Cleveland’s offense still finished with no passing touchdowns and 10 total points.
The difference between the teams’ interception totals was one. Chicago’s defense also accumulated 53 more interception-return yards than Cleveland’s.
Those figures are useful when evaluating turnover production, but return yards alone do not measure the complete value of a takeaway. The field position at the end of the return, the remaining game situation, and the outcome of the following possession all influence how much a turnover changes the scoreboard.
Chicago’s Advantage in Sacks and Tackles for Loss
Chicago recorded three sacks and seven tackles for loss. Cleveland recorded two sacks and five tackles for loss.
The Bears therefore produced one additional sack and two additional tackles for loss. Both categories indicate plays that pushed Cleveland backward and potentially disrupted its offensive rhythm.
Sacks deserve particular attention because they can turn a manageable passing situation into a difficult one. A loss of yardage can force a team into longer third downs or make a scoring drive more challenging.
Chicago’s defense also recorded five quarterback hits, matching Cleveland’s total. This shows that both defenses registered the same number of quarterback hits in the supplied statistics, even though Chicago finished with more sacks.
It would be inaccurate to conclude from these numbers alone that Chicago consistently dominated every pass-rushing matchup. The available data supports a narrower conclusion: Chicago recorded more sacks, tackles for loss, and interceptions in this game.
Turnovers and Their Effect on the Final Score
Turnovers are among the most important statistics to examine when a team loses by a large margin. In this matchup, Cleveland threw two interceptions, while Chicago threw one.
The fumble data also requires attention. Cleveland’s team total listed two fumbles, one lost and one recovered. Chicago’s team total listed no fumbles and one recovered fumble. These figures should be read carefully because a recovered fumble does not necessarily mean the recovering team forced a turnover from the opponent. The supplied summary does not provide enough detail to reconstruct every fumble sequence confidently.
Turnover Comparison
| Statistic | Cleveland Browns | Chicago Bears |
| Interceptions thrown | 2 | 1 |
| Interceptions recorded by defense | 1 | 2 |
| Fumbles listed | 2 | 0 |
| Fumbles lost | 1 | 0 |
| Interception return yards | 2 | 55 |
Cleveland’s two interceptions were particularly costly in a game where its offense did not record a passing touchdown. Chicago, meanwhile, combined three passing touchdowns with two defensive interceptions.
That combination helped the Bears establish an advantage in both offensive scoring and defensive disruption.
It is important not to assign an exact number of points to each turnover without examining the subsequent drives. Not every interception leads directly to points, and the value of a turnover depends on what happens afterward. Still, the overall pattern is clear: Chicago produced more interceptions on defense and threw fewer interceptions on offense.
For fans reviewing game statistics, turnover differential is a useful starting point. It becomes even more informative when paired with points scored, scoring drives, and the field position created by each takeaway.
Special Teams Statistics: Kicking, Punting, and Returns
Special teams often receive less attention than quarterback and receiver performances, but these plays influence field position and can contribute directly to the scoreboard.
The supplied statistics show that both teams had productive individual return performances. Chicago also scored more points through kicking, while Cleveland’s kicking unit converted its only field-goal attempt.
Kicking Statistics
| Statistic | Cleveland Browns | Chicago Bears |
| Field goals made | 1 | 2 |
| Field goals attempted | 1 | 3 |
| Field-goal percentage | 100% | 66.7% |
| Longest field goal | 50 yards | 51 yards |
| Extra points made | 1 | 4 |
| Extra points attempted | 1 | 4 |
| Kicking points | 4 | 9 |
Andre Szmyt converted Cleveland’s only field-goal attempt, a 50-yard kick, and made one extra point. He finished with four points.
Cairo Santos made two of three field-goal attempts for Chicago, with a long of 51 yards. He also converted all four extra-point attempts, finishing with nine kicking points.
Cleveland had the higher field-goal percentage, but Chicago made more field goals and extra points. This is another example of why percentages need to be considered alongside volume and total scoring.
A kicker who makes one of one attempts has a 100% success rate, but that small sample cannot establish a reliable long-term conversion rate. Similarly, Chicago’s two successful kicks on three attempts provide more opportunities to assess performance, but three attempts are still a limited sample.
Punt Returns
| Team | Player | Returns | Yards | Average | Longest |
| Cleveland | KC Concepcion | 1 | 31 | 31.0 | 31 |
| Cleveland | Gage Larvadain | 1 | 8 | 8.0 | 8 |
| Chicago | Kaden Davis | 1 | 23 | 23.0 | 23 |
| Chicago | Ray-Ray McCloud III | 1 | 11 | 11.0 | 11 |
| Chicago | Maurice Alexander | 2 | 8 | 4.0 | 6 |
Cleveland’s punt-return unit gained 39 yards on two returns, averaging 19.5 yards per return. Chicago recorded 42 yards on four returns, averaging 10.5 yards per return.
Concepcion’s 31-yard return was the longest punt return listed for either team. That was a notable contribution because a substantial return can improve field position without requiring the offense to gain those yards through a passing or rushing play.
Chicago’s aggregate punt-return average was lower, but the team had twice as many returns. These numbers should not be interpreted as proof that one unit was consistently better without considering the number and type of opportunities.
Kickoff Returns
Cleveland returned five kickoffs for 122 yards, averaging 24.4 yards per return. Chicago returned three kickoffs for 70 yards, averaging 23.3 yards per return.
Maurice Alexander led Chicago with 52 yards on two returns. Cleveland’s listed contributors included Kole Wilson, Davon Booth, Raheim Sanders, and Tylan Wallace.
Cleveland’s total kickoff-return yardage was higher, partly because it had more returns. Its average advantage was just over one yard per return.
The practical lesson is that return totals depend on both opportunity and efficiency. Comparing total yards without comparing return attempts can make a team with more opportunities appear more efficient than it actually was.
Punting Performance
Cleveland’s punters combined for five punts covering 245 yards, an average of 49.0 yards per punt. Chicago recorded four punts for 188 yards, averaging 47.0 yards per punt.
Cleveland’s listed punters included Nik Constantinou, Wes Pahl, and Corey Bojorquez. Chicago’s punting total was attributed to Tory Taylor.
Cleveland had a higher average punt distance, but distance alone does not fully measure punting effectiveness. A punt’s value also depends on where the ball lands, whether it produces a touchback, and where the opponent begins its next possession.
The supplied statistics list two Cleveland punts inside the 20-yard line and one Chicago punt inside the 20. Chicago also recorded one touchback, compared with none for Cleveland.
The available summary offers a useful overview of kicking and punting production, but it does not provide enough detail to reconstruct the field-position outcome of every special-teams play.
What the Cleveland Browns vs Chicago Bears Match Player Stats Reveal About Team Performance
The most useful way to interpret the box score is to look at how different statistical categories fit together. Cleveland won the rushing-yardage comparison, but Chicago won the passing, receiving, turnover, and final-score comparisons.
This is more informative than focusing on any single player or category.
1. Chicago Converted Passing Opportunities Into Points
Chicago’s quarterbacks combined for 348 passing yards and three touchdowns. Cleveland’s quarterbacks combined for 206 passing yards and no passing touchdowns.
That 142-yard passing difference was accompanied by a clear difference in scoring. The Bears made their passing production count by recording three touchdown passes, while Cleveland’s passing attack failed to produce a touchdown.
For fans assessing offensive execution, this distinction is important. A team can move the ball between the 20-yard lines but still struggle to finish drives. Touchdowns, interceptions, sacks, and the final score provide essential context for the yardage totals.
2. Cleveland Found Rushing Yardage but Not Enough Overall Scoring
The Browns recorded 108 rushing yards on 27 attempts, compared with Chicago’s 82 yards on 21 attempts.
Cleveland’s six additional carries produced 26 more rushing yards. That was a real statistical advantage, but it did not overcome the difference in passing production and scoring.
Booth’s 53 yards on four carries contributed substantially to Cleveland’s rushing total. His explosive runs helped raise the team average, while several other Cleveland rushers recorded more modest totals.
The broader point is that rushing yardage should be evaluated alongside touchdown production and overall offensive balance. Cleveland scored one rushing touchdown, but its quarterbacks did not throw a touchdown pass.
3. Turnovers Added Another Layer to Chicago’s Advantage
Chicago’s defense recorded two interceptions, while Cleveland’s defense recorded one. The Bears also finished with 55 interception-return yards compared with Cleveland’s two.
Cleveland’s quarterbacks threw two interceptions, and Chicago’s quarterbacks threw one. These numbers show a turnover disadvantage for the Browns in the passing game.
Turnovers are especially relevant when an offense is already struggling to score. Losing a possession can remove an opportunity to build momentum, while an interception return can improve the opposing team’s field position.
However, the statistics do not establish exactly how many points resulted from each turnover. A responsible analysis distinguishes between a confirmed turnover and the possible scoring opportunity that follows it.
4. Chicago’s Receiving Production Was More Explosive
Chicago recorded 368 receiving yards on 25 receptions. Cleveland recorded 228 yards on 22 receptions.
The Bears gained 140 more receiving yards from just three additional catches. Their 14.7 yards per reception also exceeded Cleveland’s 10.4.
Davis, McCloud, and Ahmed were particularly productive in Chicago’s passing game. Each averaged more than 18 yards per reception, while Ahmed also recorded a receiving touchdown.
Cleveland had explosive individual performances from Sampson and Floriea, but the team did not match Chicago’s overall receiving yardage or touchdown production.
5. Defensive Statistics Support the Scoreline
Chicago recorded three sacks, seven tackles for loss, and two interceptions. Cleveland recorded two sacks, five tackles for loss, and one interception.
These figures support the conclusion that Chicago produced more negative plays and takeaways. They do not prove that the Bears won every individual matchup, but they show a broader defensive advantage in several important categories.
The Browns’ nine passes defended compared with Chicago’s eight also demonstrate why the defensive comparison should not be reduced to a single statistic. Cleveland had positive defensive contributions even in a game where the final result was unfavorable.
6. The Second Half Was the Decisive Period
Cleveland scored no points in either the third or fourth quarter. Chicago scored 14 in the third quarter and 10 in the fourth.
The Bears therefore scored 24 second-half points while keeping the Browns off the scoreboard.
That pattern is one of the strongest indicators of how the game developed. Cleveland had scored 10 points by halftime, but it could not add to that total. Chicago continued scoring and expanded the margin.
The available score summary does not explain every coaching decision or substitution, so it would be inappropriate to attribute the second-half difference to a particular tactical change without additional evidence. What is clear is that Chicago sustained its scoring production after halftime while Cleveland did not.
How to Read Preseason Player Statistics Correctly
Preseason box scores can be useful, but they require careful interpretation. A player may have limited attempts, share opportunities with several teammates, or perform in situations that differ from regular-season competition.
A good analysis should separate what the numbers confirm from what they merely suggest.
Focus on Sample Size
Four carries, one reception, or eight passing attempts provide limited evidence about a player’s long-term ability. Small samples can produce unusually high or low averages because one play has a large effect on the total.
For example, Booth averaged 13.3 yards per carry on four attempts. That is an impressive result, but it does not establish that he would maintain that average across a full season.
Similarly, Floriea’s 35 yards on one catch represent a productive play, not a complete measure of receiving consistency.
Compare Volume With Efficiency
Volume measures how often a player receives opportunities. Efficiency measures what the player produces from those opportunities.
Both matter.
- A quarterback’s passing yards should be compared with attempts and sacks.
- A running back’s rushing yards should be compared with carries.
- A receiver’s yardage should be considered alongside receptions and targets.
- A kicker’s success rate should be considered alongside the number and distance of attempts.
This approach prevents a misleading conclusion based on a single impressive number.
Distinguish Individual Production From Team Success
A player can produce strong individual statistics in a losing performance. Watson’s 73.3% completion rate and Booth’s 53 rushing yards are examples of productive individual contributions within Cleveland’s 10-point result.
Conversely, a team can win without leading every category. Chicago gained fewer rushing yards than Cleveland but still won by 24 points.
Individual statistics explain contributions. Team statistics and the scoreboard explain the broader outcome.
Verify Unusual or Inconsistent Entries
Statistical accuracy is essential when publishing a sports article. If a receiver’s recorded catches exceed the number of targets, for example, the target total should be checked before calculating a catch rate.
The supplied data lists three receptions and two targets for KC Concepcion. Because receptions ordinarily cannot exceed targets, that entry should be verified against an authoritative box score.
A similar check is worthwhile whenever player totals, team totals, and derived averages appear inconsistent. It is better to flag a discrepancy than to present a calculation that looks precise but rests on questionable input.
Key Statistical Takeaways for Browns and Bears Fans
The August 15, 2026, preseason matchup produced several clear statistical findings.
- Final result: Chicago defeated Cleveland 34-10.
- Passing advantage: Chicago recorded 348 passing yards, compared with Cleveland’s 206.
- Quarterback efficiency: Keenum led the listed passers with 151 yards, two touchdown passes, no interceptions, and a 158.3 passer rating.
- Cleveland’s rushing leader: Booth gained 53 yards on four carries.
- Chicago’s leading receiver: Davis recorded 88 receiving yards on four catches.
- Cleveland’s leading receiver by yardage: Sampson gained 43 yards on two receptions.
- Defensive standout: Bishop recorded two interceptions and 55 return yards.
- Rushing comparison: Cleveland gained 26 more rushing yards than Chicago.
- Second-half difference: Chicago scored 24 second-half points, while Cleveland scored none.
- Special-teams note: Cleveland averaged 49.0 yards per punt, compared with Chicago’s 47.0.
Taken together, these figures show why Chicago won despite losing the rushing-yardage comparison. The Bears generated significantly more passing production, scored three passing touchdowns, and recorded more defensive interceptions.
For Cleveland, the statistics show a mixture of individual positives and broader offensive limitations. Booth produced explosive rushing yardage, Sampson and Floriea delivered productive receiving plays, and the defense recorded nine passes defended. However, the Browns did not record a passing touchdown and were held scoreless in the second half.
These findings describe the game itself. They should not be used on their own to predict future standings, starting lineups, or regular-season outcomes.
Frequently Asked Questions
1. What was the final score of the Cleveland Browns vs Chicago Bears game on August 15, 2026?
The Chicago Bears defeated the Cleveland Browns 34-10 in their preseason matchup on August 15, 2026. Chicago scored 24 second-half points, while Cleveland failed to score after halftime.
2. Which quarterback had the best passing statistics in the game?
Case Keenum recorded the strongest passing line among the listed quarterbacks. He completed nine of 10 passes for 151 yards and two touchdowns, threw no interceptions, and posted a 158.3 passer rating.
3. Who led the Cleveland Browns in rushing yards?
Davon Booth led Cleveland’s listed rushers with 53 yards on four carries. He averaged 13.3 yards per carry, and his longest run covered 28 yards.
4. Who led the Chicago Bears in receiving yards?
Kaden Davis led Chicago’s listed receivers with 88 yards on four receptions. He averaged 22 yards per catch, with a longest reception of 41 yards.
5. How many interceptions did each team record?
Chicago recorded two defensive interceptions, both credited to Beanie Bishop Jr., who returned them for 55 combined yards. Cleveland recorded one interception through Michael Coats Jr., with two return yards.
6. Why did Chicago win despite recording fewer rushing yards?
Chicago gained 82 rushing yards, while Cleveland gained 108. However, the Bears produced 348 passing yards and three passing touchdowns, compared with Cleveland’s 206 passing yards and no passing touchdowns. Chicago also recorded more defensive interceptions and scored 24 second-half points, which helped determine the final result.
Conclusion
The Cleveland Browns vs Chicago Bears Match Player Stats show that Chicago’s 34-10 preseason victory was built on superior passing production, explosive receiving plays, and defensive takeaways. The Bears recorded 348 passing yards, three passing touchdowns, and two interceptions on defense. Cleveland gained more rushing yards, but its 108-yard ground total was not enough to compensate for the difference in passing production and scoring.
Case Keenum delivered the most efficient quarterback performance in the supplied statistics, completing 90% of his passes for 151 yards and two touchdowns. Kaden Davis led Chicago’s receivers with 88 yards, while Davon Booth topped Cleveland’s rushers with 53 yards on four carries. Beanie Bishop Jr. also made a major defensive contribution with two interceptions.
The game illustrates why a complete statistical review must go beyond the final score. Passing efficiency, rushing production, receiving averages, turnovers, sacks, and special-teams results each provide a different perspective on performance. No single category tells the whole story.
The most important finding is that Chicago converted its passing opportunities into points and maintained its advantage after halftime. Cleveland produced several encouraging individual statistical performances, but its inability to score in the second half left the team without a realistic route back into the contest.
Because this was a preseason game, the results should be treated as a record of that day’s performance rather than a definitive forecast of either team’s regular-season prospects. Individual opportunities were limited, and further games would be needed to establish consistent trends.
