Green Bay Packers vs Minnesota Vikings Match Player Stats

When fans search for green bay packers vs minnesota vikings match player stats, they usually want more than a final score. They want to know which quarterbacks are producing, which running backs are carrying the offense, which receivers are creating the biggest plays, and which defenders are making an impact. The supplied ESPN matchup data provides a useful snapshot of those areas ahead of the November 15, 2026 meeting between the Minnesota Vikings and Green Bay Packers at Lambeau Field.

This matchup is scheduled for Week 10 of the 2026 NFL regular season, with Minnesota visiting Green Bay on Sunday, November 15. The official NFL schedule lists a 1:00 PM ET kickoff and FOX as the television network.

The numbers supplied for this analysis show an interesting contrast. Minnesota enters the snapshot at 2-0, while Green Bay is listed at 1-1. The Vikings have also been more efficient defensively in the supplied standings, allowing 25 points compared with 56 for the Packers.

At the same time, Green Bay’s individual passing and receiving numbers are substantial. Jordan Love is listed with 532 passing yards and four touchdowns, while Christian Watson leads the Packers with 188 receiving yards and three touchdowns.

That creates an important distinction between team-level results and individual production. A player can produce impressive statistics without his team winning every game, and a team can win while relying on several players rather than one dominant statistical performer.

This article breaks down those numbers, explains what they mean, identifies the most important statistical matchups, and provides context for readers who want a clearer picture of the game.

Table of Contents

green bay packers vs minnesota vikings match player stats

The supplied matchup snapshot gives the following headline numbers:

Category	Minnesota Vikings	Green Bay Packers
Record	2-0	1-1
Away/Home Record	1-0 away	0-0 home
Passing Leader	C. Wentz: 276 yards, 23/39, 3 TD	J. Love: 532 yards, 37/71, 4 TD, 1 INT
Rushing Leader	A. Jones Sr.: 145 yards, 35 carries, 1 TD	M. Lloyd: 57 yards, 19 carries
Receiving Leader	J. Jefferson: 147 yards, 11 receptions, 2 TD	C. Watson: 188 yards, 10 receptions, 3 TD
Sacks Leader	D. Turner: 3	L. Van Ness: 2.5
Tackles Leader	B. Cashman: 17	E. Williams: 19
Supplied NFC North Record	2-0	1-1
Points For	48	42
Points Against	25	56

One detail deserves attention immediately: these are not simply box-score numbers from a completed Vikings-Packers game. They are matchup data supplied for the November 15 meeting. That distinction matters because the numbers describe the players’ statistical position in the available snapshot rather than an official final-game box score.

For readers researching green bay packers vs minnesota vikings match player stats, keeping that distinction clear prevents one of the most common problems with sports content: mixing pregame statistics with postgame statistics.

Jordan Love vs C. Wentz: The Quarterback Statistical Picture

Quarterback statistics naturally attract the most attention in an NFL matchup, and this game has a particularly interesting contrast.

The supplied data lists Jordan Love with 532 passing yards, 37 completions on 71 attempts, four touchdown passes, and one interception.

Wentz is listed with 276 passing yards, 23 completions on 39 attempts, and three touchdown passes.

At first glance, Love has the larger passing volume by a considerable margin. He has attempted 71 passes compared with 39 for Wentz. That difference is important because raw passing yardage is heavily influenced by opportunity.

Love’s listed completion rate is approximately 52.1 percent:

37 completions divided by 71 attempts equals approximately 52.1 percent.

Wentz’s listed completion rate is approximately 59.0 percent:

23 completions divided by 39 attempts equals approximately 59.0 percent.

This does not by itself establish that one quarterback is playing better. Completion percentage can be affected by passing depth, game situation, pressure, receiver separation, offensive design, and the number of difficult throws attempted.

The touchdown numbers are also close despite the difference in passing volume. Love has four touchdown passes, while Wentz has three.

The interception column provides another useful piece of context. Love is listed with one interception. No interception is listed for Wentz in the supplied data.

That means a simple reading of the numbers would be incomplete. Love has generated substantially more passing volume, while Wentz has the higher completion percentage in the supplied snapshot.

What the quarterback numbers actually tell us

There are three major points to take from this comparison.

First, Green Bay’s passing game has been heavily involved. Seventy-one attempts for Love indicates a substantial passing workload.

Second, Minnesota’s listed quarterback numbers show fewer attempts but a higher completion rate.

Third, touchdown production is relatively close despite the difference in attempts.

For a deeper statistical evaluation, readers should eventually examine yards per attempt, passing first downs, sack rate, passer rating, expected points added, and performance under pressure. Those numbers can reveal whether the difference comes primarily from volume or from efficiency.

The supplied dataset does not provide all of those measurements, so it would be misleading to invent them.

Aaron Jones Sr. and MarShawn Lloyd Lead the Ground Game

The rushing comparison provides another major storyline.

Minnesota’s A. Jones Sr. is listed with 145 rushing yards on 35 carries and one touchdown.

Green Bay’s M. Lloyd is listed with 57 rushing yards on 19 carries.

That produces a major difference in rushing volume and yardage.

Jones Sr.’s listed average is approximately 4.14 yards per carry:

145 yards divided by 35 attempts.

Lloyd’s listed average is exactly 3.00 yards per carry:

57 yards divided by 19 attempts.

This is one of the clearest statistical differences in the supplied matchup information.

Minnesota has received more production from its listed leading rusher, both in total yardage and average yards per attempt.

The difference is not merely about explosive plays. Thirty-five rushing attempts represent significant workload. A running back receiving that many carries can influence the structure of an entire game because repeated rushing attempts can help control the clock and create manageable passing situations.

Green Bay’s 19 carries for Lloyd represent a much smaller workload.

Why rushing efficiency matters

Rushing yards alone can sometimes hide what is happening on the field.

For example, a running back might accumulate 100 yards through a combination of one long run and several short gains. Another might reach 100 yards through consistent four- and five-yard gains.

That is why the 4.14 yards per carry listed for Jones Sr. is useful context. It suggests that his production is not simply the result of an unusually large workload.

Lloyd’s three yards per carry, meanwhile, indicates that Green Bay’s listed leading rusher has produced less yardage per opportunity in the supplied sample.

However, these numbers should not be treated as a complete evaluation of either rushing attack. Offensive line play, defensive fronts, down-and-distance situations, blocking schemes, red-zone opportunities, and quarterback rushing can all affect team rushing performance.

Justin Jefferson vs Christian Watson: The Receiving Battle

The receiving statistics may be the most exciting part of this matchup.

Minnesota’s J. Jefferson is listed with 147 receiving yards, 11 receptions, and two touchdowns.

Green Bay’s C. Watson is listed with 188 receiving yards, 10 receptions, and three touchdowns.

Watson therefore leads the supplied receiving leaders in total yardage and touchdowns, while Jefferson has one more reception.

The raw numbers are:

Justin Jefferson

11 receptions
147 receiving yards
2 touchdowns

Christian Watson

10 receptions
188 receiving yards
3 touchdowns

Watson’s listed average receiving yardage per catch is 18.8 yards.

Jefferson’s listed average is approximately 13.4 yards per reception.

That difference is significant.

It suggests that, within the supplied data, Watson has generated more yardage per completed reception. Jefferson, however, has been targeted successfully more often in terms of receptions.

This creates two different forms of receiving production.

Jefferson’s numbers show high-volume involvement.

Watson’s numbers show fewer receptions but greater yardage and touchdown production.

Neither statistic should be considered in isolation.

Why reception totals can be misleading

A receiver catching 11 passes is not automatically more productive than one catching 10.

The bigger question is what those receptions accomplish.

Watson’s 188 yards on 10 catches means each reception has produced substantial yardage on average. Jefferson’s 147 yards on 11 receptions points toward a slightly more volume-oriented receiving profile in the supplied data.

Touchdowns add another layer.

Watson is listed with three touchdown receptions compared with two for Jefferson.

That means Watson has contributed more directly to scoring through the supplied receiving statistics.

However, receiving production is strongly dependent on offensive design and quarterback behavior. A receiver can have fewer catches because his team runs more frequently, because defenses double-team him, or because the offense distributes targets among several players.

For that reason, the most useful approach is to read these numbers as evidence of how the offenses are distributing production rather than as a complete measure of individual ability.

The Defensive Leaders: Sacks and Tackles

Offensive statistics usually dominate searches for matchup information, but defensive numbers can explain why an offense succeeds or struggles.

The supplied data lists D. Turner as Minnesota’s sack leader with three sacks.

Green Bay’s L. Van Ness is listed with 2.5 sacks.

The difference is only half a sack, making this category much closer than the passing or rushing comparisons.

Tackles provide another contrast.

Minnesota’s B. Cashman is listed with 17 tackles.

Green Bay’s E. Williams is listed with 19 tackles.

The Packers therefore have the higher listed tackle total from their leading tackler.

That does not necessarily mean Green Bay has the stronger defense. High tackle totals can occur when a defense is on the field frequently or when opposing plays reach the second level.

This is an important statistical principle: more tackles are not automatically evidence of better defensive performance.

Similarly, sack totals do not explain everything about pass defense.

Pressure rate, quarterback hits, hurry rate, coverage performance, and third-down defense would provide additional context. Those figures are not included in the supplied dataset, so they should not be invented.

Team Form Before the Matchup

The supplied last-five-game records add context to the individual numbers.

Minnesota’s listed sequence is:

W 9-3 vs Chicago
W 39-22 vs Green Bay
L 6-34 at Denver
L 3-13 vs Baltimore
W 13-10 at New York Giants

Green Bay’s listed sequence is:

W 20-17 OT at New York Jets
L 22-39 at Minnesota
W 42-38 vs Arizona
W 33-13 at Denver
L 9-28 at Pittsburgh

There is an important limitation here.

The supplied list contains results that extend beyond the currently established early-season point shown in the schedule. Therefore, these figures should be treated as the provided statistical dataset rather than independently presented as a completed historical sequence.

That distinction is particularly important for sports publishing.

A good statistical article should tell readers whether a number represents:

A completed game.
A current-season total.
A historical average.
A projection.
A supplied snapshot.
A live or pregame statistic.

Mixing those categories makes an article look authoritative while making it harder for readers to understand what they are actually seeing.

What the NFC North Numbers Show

The supplied NFC North figures list Minnesota at 2-0 with 48 points scored and 25 allowed.

Green Bay is listed at 1-1 with 42 points scored and 56 allowed.

That creates a 23-point positive scoring margin for Minnesota:

48 minus 25 equals +23.

Green Bay’s listed margin is negative 14:

42 minus 56 equals -14.

The difference between those margins is 37 points.

Again, that should not be interpreted as a prediction of what will happen in the November meeting. It is simply a description of the supplied team-level numbers.

The figures do, however, help explain why the matchup is statistically interesting.

Minnesota has the better record and lower points allowed in the supplied snapshot.

Green Bay has significant individual passing and receiving production.

That combination sets up a classic team-versus-individual-production question.

Previous Vikings-Packers Meeting in the Supplied Data

The supplied last-five-game information includes a 39-22 Minnesota victory over Green Bay.

The official NFL game center also records a 39-22 Minnesota victory over Green Bay in Week 1 of the 2026 regular season.

That result matters because the teams are scheduled to meet again in Green Bay on November 15.

A rematch creates a different statistical environment from a first-time meeting.

Coaches have access to film from the previous game. Players have already experienced the opponent’s defensive structures and offensive concepts. Adjustments can therefore become just as important as raw statistical production.

For readers looking at green bay packers vs minnesota vikings match player stats, this is one reason not to rely only on the previous final score.

The second meeting can develop differently because both teams have more information about the other.

Injury Information and Why It Matters

The supplied injury list includes several players on both teams.

Minnesota Vikings injuries

The provided data lists:

DeeJay Dallas: Questionable, toe
Jalen Redmond: Questionable, elbow
Nick Samac: Questionable, knee
Brian O’Neill: Questionable, knee
Jake Golday: Questionable, ankle

Green Bay Packers injuries

The provided data lists:

Aaron Banks: Out, toe
Warren Brinson: Out, calf
Javon Hargrave: Questionable, concussion
Jayden Reed: Out, neck
Zach Bako-Bewele: Out, knee

Injury designations are particularly important when interpreting player statistics because the absence of one player can change opportunities for another.

For example, the absence of a receiver can increase target opportunities for another receiver. An offensive-line injury can influence rushing lanes and quarterback pressure. A defensive-line absence can alter how frequently a defense generates pressure with its front four.

But an injury report should not be used to claim a definite statistical outcome.

Questionable means there is uncertainty surrounding availability, and even an active player may have a different workload depending on the circumstances.

For accurate pregame publishing, injury information should be checked again close to kickoff.

How to Read These Statistics Without Overreacting

One of the biggest mistakes in sports analysis is treating a single statistical category as the answer to the entire matchup.

Football is too interconnected for that.

A quarterback’s numbers depend on protection.

Protection depends partly on offensive line health and defensive pressure.

Passing success affects defensive alignment.

Defensive alignment affects rushing opportunities.

Rushing success affects play action and down-and-distance.

And all of these factors influence individual player statistics.

That is why the most useful reading of this matchup combines multiple categories.

Passing volume

Love’s 71 attempts stand out.

Passing efficiency

Wentz’s listed 59.0 percent completion rate is higher than Love’s 52.1 percent.

Rushing production

Jones Sr.’s 145 yards lead the supplied rushing statistics.

Receiving production

Watson’s 188 yards and three touchdowns lead the supplied receiving numbers.

Defensive pressure

Turner’s three sacks and Van Ness’s 2.5 sacks show that both teams have players capable of creating pressure.

Defensive activity

Williams and Cashman are the listed tackle leaders for their respective teams.

Taken together, these numbers provide a much more complete picture than focusing on one statistic.

green bay packers vs minnesota vikings match player stats: The Numbers to Watch

For anyone following the matchup, several statistical checkpoints deserve special attention.

1. Jordan Love’s efficiency

The supplied passing volume is already high. The important question is how efficiently Green Bay converts those opportunities.

Completion percentage is one measure, but yards per attempt, touchdown rate, interception rate, and third-down passing would provide additional insight.

2. C. Wentz’s ability to sustain drives

Wentz’s supplied completion rate is higher than Love’s. The next question is whether Minnesota can turn completions into first downs and scoring opportunities.

3. Aaron Jones Sr.’s workload

Thirty-five carries is a substantial rushing workload in the supplied data.

Whether Minnesota continues to rely heavily on its rushing attack would be an important development.

4. Christian Watson’s explosive production

Watson’s 18.8 yards per reception stands out.

If that level of explosive production continues, it would indicate that Green Bay is generating substantial yardage from relatively few completed passes to its leading receiver.

5. Justin Jefferson’s involvement

Jefferson has 11 receptions in the supplied snapshot.

His continued involvement would be important for understanding how Minnesota distributes its passing offense.

6. Pass rush

Turner and Van Ness are separated by only half a sack in the supplied statistics.

Pressure on the quarterback could become especially important if either offense relies heavily on passing.

Public Picks and the Matchup Predictor

The supplied ESPN data lists a matchup predictor of 49.5 percent for Minnesota and 50.1 percent for Green Bay.

It also lists public picks at 18 percent for Minnesota and 82 percent for Green Bay.

These numbers should be treated differently from player statistics.

A matchup predictor is a model output, while public picks reflect selections made by a group of people. Neither is a player performance statistic.

More importantly, these figures do not establish what will happen in the game.

For an informational article, the useful role of these numbers is to show how closely balanced the supplied matchup predictor is and how different the public selection distribution appears to be.

That contrast is worth noticing because public opinion and statistical models can produce very different distributions.

Why the Venue Matters

The game is scheduled for Lambeau Field in Green Bay.

Home-field context is relevant when comparing team statistics, but it should not be used as a shortcut for predicting the result.

The supplied Packers record is listed as 0-0 at home because the snapshot comes very early in the season.

That means there is not enough home-game data in the supplied record to make a meaningful statistical conclusion about Green Bay’s 2026 home performance.

This is another example of why context matters.

A statistic can be technically correct while still being too small a sample to support a broad conclusion.

What Fans Should Look for During the Game

If you are watching the matchup and want to understand the statistics as they develop, focus on several observable areas.

Watch the early-down offense

First-down efficiency can determine whether an offense faces manageable second and third downs.

Watch pressure on the quarterback

Sacks are easy to count, but pressure that does not result in a sack can also alter a quarterback’s decisions.

Watch how defenses treat the top receivers

Jefferson and Watson are the leading receiving names in the supplied data.

Double coverage, safety help, press coverage, and defensive alignment can all influence their final numbers.

Watch rushing efficiency rather than rushing totals alone

A team can accumulate rushing yards late in a game because the opponent is protecting a lead.

Looking at rushing efficiency throughout the game provides better context.

Watch red-zone execution

Touchdowns matter more than empty yardage.

A team that consistently moves between the 20-yard lines but settles for field goals may produce strong yardage numbers without matching its offensive efficiency in scoring situations.

The Most Important Statistical Lesson From This Matchup

The biggest lesson from these numbers is that individual production and team performance are not identical.

Green Bay’s supplied numbers include 532 passing yards from Love and 188 receiving yards from Watson.

Minnesota’s numbers include 145 rushing yards from Jones Sr. and 147 receiving yards from Jefferson.

Those statistics show meaningful individual production on both sides.

But team records depend on much more than the leading numbers in a box score.

Turnovers, field position, third-down conversion, red-zone efficiency, penalties, special teams, defensive stops, and situational execution all affect the final result.

That is why a responsible player-stat article should avoid declaring that one statistical leader automatically determines the outcome.

Instead, the statistics should be used to identify the areas most likely to shape the game.

A Practical Way to Compare the Two Teams

Readers can use a simple framework when evaluating this matchup.

Quarterback: Compare volume, completion rate, touchdowns, interceptions, and efficiency.

Running back: Compare attempts, yards, yards per carry, and touchdowns.

Receiver: Compare receptions, yards, yards per catch, and touchdowns.

Pass rush: Compare sacks and, when available, quarterback pressures.

Tackling: Compare tackle totals while considering defensive snap volume.

Team scoring: Compare points scored and points allowed.

Availability: Recheck injury designations before kickoff.

This approach is more useful than simply asking which team has the bigger number in one category.

Important Data Caveat

The supplied data contains a mixture of current-looking records, player statistics, matchup information, injuries, recent results, public picks, and a scheduled future game.

Because the November 15, 2026 matchup has not yet occurred at the time of this article’s preparation, the article does not present a final score or claim that the supplied player numbers are statistics recorded during that specific game.

That distinction is essential.

Once the game is completed, a postgame version should replace the preview language with the official box score and should clearly identify passing, rushing, receiving, defensive, kicking, and team statistics from the completed contest.

For readers checking green bay packers vs minnesota vikings match player stats, the date of the data is therefore just as important as the numbers themselves.

Frequently Asked Questions

What are the key player stats for the Vikings vs Packers matchup?

The supplied matchup data lists C. Wentz with 276 passing yards and three touchdowns for Minnesota, while Jordan Love has 532 passing yards, four touchdowns, and one interception for Green Bay. A. Jones Sr. leads Minnesota in rushing with 145 yards, while M. Lloyd has 57 rushing yards for Green Bay.

Who leads the Vikings in receiving?

Jefferson is the listed Minnesota receiving leader with 11 receptions for 147 yards and two touchdowns.

Who leads the Packers in receiving?

Watson leads the supplied Green Bay receiving statistics with 10 receptions, 188 yards, and three touchdowns.

Who has the most rushing yards?

Jones Sr. leads the supplied rushing statistics with 145 yards on 35 carries and one touchdown. M. Lloyd is listed with 57 yards on 19 carries for Green Bay.

Who leads the Vikings and Packers in sacks?

Turner leads Minnesota with three sacks, while L. Van Ness leads Green Bay with 2.5 sacks in the supplied data.

When and where is Vikings vs Packers scheduled?

The 2026 regular-season matchup is scheduled for Sunday, November 15, at Lambeau Field in Green Bay. The official NFL schedule lists a 1:00 PM ET kickoff on FOX.

Conclusion

The available numbers make the Vikings-Packers matchup particularly interesting because the statistical story is not built around one category.

Minnesota enters the supplied snapshot at 2-0, with 48 points scored and 25 allowed. Green Bay is listed at 1-1, with 42 points scored and 56 allowed.

At the individual level, Jordan Love has the larger passing workload, while C. Wentz has the higher listed completion percentage. A. Jones Sr. leads the rushing comparison with 145 yards, while Christian Watson leads the receiving comparison with 188 yards and three touchdowns. Justin Jefferson remains highly involved with 11 receptions for 147 yards and two scores.

The defensive numbers are close in the supplied sack comparison, with D. Turner at three and L. Van Ness at 2.5. The tackle leaders are also separated by only two tackles, with E. Williams at 19 and B. Cashman at 17.

The most important point is that these figures should be understood as a statistical snapshot rather than a completed box score for the November 15 meeting. The official schedule confirms that the Vikings are scheduled to visit Green Bay in Week 10.

For readers studying green bay packers vs minnesota vikings match player stats, the best approach is to look beyond isolated numbers. Passing volume, rushing efficiency, receiving production, defensive pressure, injuries, scoring margin, and situational performance all contribute to the larger picture.

That is what makes NFL statistics useful. They do not simply tell us who accumulated yards. They help explain how each team’s offense and defense are functioning, where production is coming from, and which individual matchups deserve the closest attention.