Counting the dead: How statistics can find unreported killings – International Consortium of Investigative Journalists

Counting the dead: How statistics can find unreported killings - International Consortium of Investigative Journalists nevin manimala

When investigative journalist Sheila Coronel and her team began counting drug-related killings in the Philippines last year, they encountered a problem: Many of the people who had been killed in President Rodrigo Duterte’s brutal war on drugs didn’t show up in police records or media reports. In some cases, even the local priests hadn’t heard about their deaths. One priest told the reporters he’d only learned about a homicide when he smelled a rotting corpse and followed the stench to it.

How many other killings had gone unreported? Coronel and her team wondered.

The journalists enlisted the help of Patrick Ball, a statistician with the San Francisco-based Human Rights Data Analysis Group.

Everyone who has been murdered should be remembered. – Patrick Ball

Ball analyzed the data reporters had collected from a variety of sources – including on-the-ground interviews, police records, and human rights groups – and used a statistical technique called multiple systems estimation to roughly calculate the number of unreported deaths in three areas of the capital city Manila.

The team discovered that the number of drug-related killings was much higher than police had reported. The journalists, who published their findings last month in The Atlantic, documented 2,320 drug-linked killings over an 18-month period, approximately 1,400 more than the official number. Ball’s statistical analysis, which estimated the number of killings the reporters hadn’t heard about, found that close to 3,000 people could have been killed – more than three times the police figure.

Ball said there are both moral and technical reasons for making sure everyone who has been killed in mass violence is counted.

“The moral reason is because everyone who has been murdered should be remembered,” he said. “A terrible thing happened to them and we have an obligation as a society to justice and to dignity to remember them.”

Patrick Ball explains a new model for multiple systems estimation to some visitors to the Human Rights Data Analysis Group.

Counting the dead: How statistics can find unreported killings - International Consortium of Investigative Journalists nevin manimala

Ball first began applying data analysis to human rights violations in the early 1990s when he traveled to El Salvador with Peace Brigades International, a group that accompanies local activists. A Salvadoran church asked Ball for help indexing files and he ended up creating a database of crimes reported to the non-governmental Human Rights Commission. Ball was able to compare that database with the career histories of Salvadoran military officers to determine who was in charge in a particular area when a crime took place.

Since then, the Human Rights Data Analysis Group has used statistics to estimate the number of killings in conflicts around the world, including the civil war in Syria, and to calculate the number of homicides committed by police in the United States. Ball also served as an expert witness in the genocide case against Guatemala’s General Efraín Ríos Montt and had to leave the country when he and other witnesses faced threats.

Although Ball has worked with journalists in the past, his collaboration with Coronel and her team was his first experience being closely involved in a journalistic investigation. Ball said he sees an opportunity for more collaboration between reporters and statisticians.

“I always urge journalists not to try to do statistics on their own,” he said.

Sheila Coronel goes through records of drug-related killings in the Philippines.

Counting the dead: How statistics can find unreported killings - International Consortium of Investigative Journalists nevin manimala

Coronel, the director of the Toni Stabile Center for Investigative Journalism at Columbia University, agreed that data scientists can play an important role in investigative journalism.

“A lot of the work that investigative journalists do is trying to figure out the magnitude of the wrongdoing,” she said. “There are limits with what we can do with documents and data given the lack of documents and the lack of data. I think machine learning and statistical modeling provides a way for us to be able to get a bigger grasp of the problems we are investigating.”

Natural resources specialist studies statistics to improve wildlife conservation – Nevada Today

Natural resources specialist studies statistics to improve wildlife conservation - Nevada Today nevin manimala
Natural resources specialist studies statistics to improve wildlife conservation - Nevada Today nevin manimala

University of Nevada, Reno welcomes Perry Williams as a new assistant professor in the College of Agriculture, Biotechnology & Natural Resources.

Williams concentrates his research on statistical and mathematical methods for estimating population processes to improve wildlife management and conservation. He has received grants from the National Park Service and the U.S. Fish and Wildlife Service for his research on sea otters in southeast Alaska, and the U.S. Fish and Wildlife Service for research on common raven movement and habitat use.

Before he arrived at the University, he worked with wildlife in Alaska, studying various species and researching wildlife management conservation. He plans to perform similar research here at the University, as well as start investigating the wildlife conservation of sage grouse in Nevada, and waterfowl in the Suisun Marsh, California.

“I decided to work at the University of Nevada, Reno because of the great colleagues and collaborators in the department,” said Williams, who is in the College’s Department of Natural Resources & Environmental Sciences. “The opportunity to work with state and federal agencies was also a highlight.”

 He wants to continue his research as part of the College’s Nevada Agricultural Experiment Station by starting and running a lab in statistical ecology to address natural resource management in Nevada.

“Perry Williams brings to the University a unique skill set in statistical and mathematical modeling as applied to the conservation of wildlife populations,” said Peter Weisburg, chair of the Department of Natural Resources & Environmental Science. “His expertise adds to a growing cadre of applied scientists with cutting-edge quantitative skills, and we are very excited to welcome him to our faculty.”

A new book shows how not to fall for dubious statistics – Science News

A new book shows how not to fall for dubious statistics - Science News nevin manimala
A new book shows how not to fall for dubious statistics - Science News nevin manimala

The Art of Statistics
David Spiegelhalter
Basic Books, $32

There are, as the saying goes, three kinds of lies: lies, damned lies and statistics. David Spiegelhalter is here to keep you from being duped by data.

If you’re seeking a plain-language intro to statistics, or just want to get better at judging the reliability of numbers in the news, Spiegelhalter’s The Art of Statistics is a solid crash course. The book is less about learning how to use specific mathematical tools than it is about exploring the myriad ways statistics can help solve real-world problems — and why statistical claims often have to be padded with caveats.

Spiegelhalter, a statistician at the University of Cambridge, keeps things lively by tying new concepts to questions. For instance, should you fret that eating bacon will increase your risk of bowel cancer? The relative risk might make you think so: People who eat a bacon sandwich every day have an 18 percent higher risk of bowel cancer than those who don’t. But looking at the absolute risk — a rise of 6 to 7 cases per 100 people — may put your mind at ease.

Spiegelhalter’s narration is encouraging, and he knows where beginners are likely to get tripped up. He makes dense sections easier to parse by including frequent recaps and lots of data visualizations, and tucking equations into footnotes.

The Art of Statistics is alight with his enthusiasm for how statistics can be used to glean information for court cases, city planning and a host of other sectors. But Spiegelhalter warns readers not to forget the assumptions and uncertainties inherent in any analysis, and tells many cautionary tales about the ways statistics can go astray. Patchy samples and logical missteps can lead to faulty conclusions. And bad-faith statistical practices have contributed to the reproducibility crisis in psychology and other areas of science (SN: 4/2/16, p. 8). Perhaps the most flagrant example is how social psychologist Daryl Bem manipulated study designs and cherry-picked data to publish statistically significant results in 2011 that suggested humans have extrasensory perception.

Spiegelhalter doesn’t let the media off the hook, either. Many of the questions he uses to introduce topics are drawn from misleading news reports. Such debunked articles include one claiming that going to college increases your risk of getting a brain tumor — which mistook correlation for causation in data on socioeconomic status and tumor diagnoses — and another where confusing risks and ratios caused a media outlet to state that a cholesterol medication increased risk of muscle pain by up to 20, not 2, percent.

The Art of Statistics leaves readers with a better handle on the ins and outs of data analysis, as well as a heightened awareness that, as Spiegelhalter writes, “Numbers may appear to be cold, hard facts, but … they need to be treated with delicacy.”


Buy The Art of Statistics from Amazon.com. Science News is a participant in the Amazon Services LLC Associates Program. Please see our FAQ for more details.

Research advances noise cancelling for quantum computers

A team from Dartmouth College and MIT has designed and conducted the first lab test to successfully detect and characterize a class of complex, “non-Gaussian” noise processes that are routinely encountered in superconducting quantum computing systems.

The characterization of non-Gaussian noise in superconducting quantum bits is a critical step toward making these systems more precise.

The joint study, published in Nature Communications, could help accelerate the realization of quantum computing systems. The experiment was based on earlier theoretical research conducted at Dartmouth and published in Physical Review Letters in 2016.

“This is the first concrete step toward trying to characterize more complicated types of noise processes than commonly assumed in the quantum domain,” said Lorenza Viola, a professor of physics at Dartmouth who led the 2016 study as well as the theory component of the present work. “As qubit coherence properties are being constantly improved, it is important to detect non-Gaussian noise in order to build the most precise quantum systems possible.”

Quantum computers differ from traditional computers by going beyond the binary “on-off” sequencing favored by classical physics. Quantum computers rely on quantum bits — also known as qubits — that are built out of atomic and subatomic particles.

Essentially, qubits can be placed in a combination of both “on” and “off” positions at the same time. They can also be “entangled,” meaning that the properties of one qubit can influence another over a distance.

Superconducting qubit systems are considered one of the leading contenders in the race to build scalable, high-performing quantum computers. But, like other qubit platforms, they are highly sensitive to their environment and can be affected by both external noise and internal noise.

External noise in quantum computing systems could come from control electronics or stray magnetic fields. Internal noise could come from other uncontrolled quantum systems such as material impurities. The ability to reduce noise is a major focus in the development of quantum computers.

“The big barrier preventing us from having large-scale quantum computers now is this noise issue.” said Leigh Norris, a postdoctoral associate at Dartmouth that co-authored the study. “This research moves us toward understanding the noise, which is a step toward cancelling it, and hopefully having a reliable quantum computer one day.”

Unwanted noise is often described in terms of simple “Gaussian” models, in which the probability distribution of the random fluctuations of noise creates a familiar, bell-shaped Gaussian curve. Non-Gaussian noise is harder to describe and detect because it falls outside the range of validity of these assumptions and because there may simply be less of it.

Whenever the statistical properties of noise are Gaussian, a small amount of information can be used to characterize the noise — namely, the correlations at only two distinct times, or equivalently, in terms of a frequency-domain description, the so-called “noise spectrum.”

Thanks to their high sensitivity to the surrounding environment, qubits can be used as sensors of their own noise. Building on this idea, researchers have made progress in developing techniques for identifying and reducing Gaussian noise in quantum systems, similar to how noise-cancelling headphones work.

While not as common as Gaussian noise, identifying and cancelling non-Gaussian noise is an equally important challenge toward optimally designing quantum systems.

Non-Gaussian noise is distinguished by more complicated patterns of correlations that involve multiple points in time. As a result, much more information about the noise is required in order for it to be identified.

In the study, researchers were able to approximate characteristics of non-Gaussian noise using information about correlations at three different times, corresponding to what is known as the “bispectrum” in the frequency domain.

“This is the first time that a detailed, frequency-resolved characterization of non-Gaussian noise has been able to be done in a lab with qubits. This result significantly expands the toolbox that we have available for doing accurate noise characterization and therefore crafting better and more stable qubits in quantum computers,” said Viola.

A quantum computer that cannot sense non-Gaussian noise could be easily confused between the quantum signal it is supposed to process and unwanted noise in the system. Protocols for achieving non-Gaussian noise spectroscopy did not exist until the Dartmouth study in 2016.

While the MIT experiment to validate the protocol won’t immediately make large-scale quantum computers practically viable, it is a major step toward making them more precise.

“This research started on the white board. We didn’t know if someone was going to be able to put it into practice, but despite significant conceptual and experimental challenges, the MIT team did it,” said Felix Beaudoin, a former Dartmouth postdoctoral student in Viola’s group who also played an instrumental role in bridging between theory and experiment in the study.

“It’s been an absolute joy to collaborate with Lorenza Viola and her fantastic theory team at Dartmouth,” said William Oliver, a professor of physics at MIT. “We’ve been working together for years now on several projects and, as quantum computing transitions from scientific curiosity to technical reality, I anticipate the need for more such interdisciplinary and interinstitutional collaboration.”

According to the research team, there are still years of additional work required in order to perfect the detection and cancellation of noise in quantum systems. In particular, future research will move from a single-sensor system to a two-sensor system, enabling the characterization of noise correlations across different qubits.

statistics; +44 new citations

statistics; +44 new citations Report, nevin manimala
statistics; +44 new citations Report, nevin manimala

Tortella-Feliu M, Fullana MA, Pérez-Vigil A, Torres X, Chamorro J, Littarelli SA, Solanes A, Ramella-Cravaro V, Vilar A, González-Parra JA, Andero R, Reichenberg PA, Mataix-Cols PD, Vieta E, Fusar-Poli P, Ioannidis PJPA, Stein PMB, Radua J, de la Cruz LF.

Neurosci Biobehav Rev. 2019 Sep 11. pii: S0149-7634(19)30601-3. doi: 10.1016/j.neubiorev.2019.09.013. [Epub ahead of print] Review.

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Khalil Mack’s impact on Bears can’t be measured by statistics – Chicago Sun-Times

Khalil Mack’s impact on Bears can’t be measured by statistics - Chicago Sun-Times nevin manimala
Khalil Mack’s impact on Bears can’t be measured by statistics - Chicago Sun-Times nevin manimala

Any team would like to have outside linebacker Khalil Mack running roughshod through the line of scrimmage, but it takes more than that to become beloved in an organization.

Not that the Bears needed any further confirmation that their daring trade for Mack a year ago was the right move, but he has continued to reveal himself to be an ideal superstar. He’s not here to count statistics and stack up awards.

‘‘You could care about statistics, but statistics for me are stacking wins,’’ he said Friday. ‘‘You want to build and stack wins, do anything you can to put the team in position to win the ballgame. That’s what I’m all about.’’

Mack’s season opened with a meager five tackles and no other statistics of note, but he’s beyond getting validation from numbers. He played well enough that he could have come away with at least a couple of sacks, but his efforts cleared the way for Leonard Floyd and others instead.

His position coach, Ted Monachino, said Mack had a major impact on 90 percent of his snaps and said he played ‘‘as well as I’ve ever had a guy play in a game.’’ Floyd deflected credit for one of his sacks, saying, ‘‘Khalil gave it to me, honestly,’’ after Mack came within inches of getting his hand on Packers quarterback Aaron Rodgers.

‘‘You want to be in position to make plays, but ultimately it’s about the team and about winning,’’ Mack said. ‘‘If another guy makes the play, I’m happy he made a good play. Because I wouldn’t be able to live with myself if [Rodgers] got away and made a big play.’’

That’s going to happen a lot with someone as prominent as Mack.

‘‘They have to scheme for No. 52,’’ coach Matt Nagy said. ‘‘They have to. Every team does. It’s no secret. So when you scheme for a guy, there’s an advantage somewhere else.’’

It feels naive to fawn over an athlete’s selflessness when many eventually show themselves to be the opposite, but Mack is so believable.

And consistent. It was clear from that first day in Bourbonnais that he was in the right mindset for the Bears’ season of astronomical expectations.

The organization is desperate after 33 seasons without a championship and 12 without reaching the Super Bowl. That resonates with Mack. He strung together five exceptional seasons to start his career but won zero playoff games. He’s grounded enough to be realistic and acknowledge he has no idea how long his prime will last.

Those great individual seasons seem somewhat empty to him at 28, and he senses he might never get as good an opportunity to win big as he has now.

So he has no interest in checking his stats, nor does he bother wading into a potential blame game with the offense after the Bears squandered a sterling defensive effort in their 10-3 loss to the Packers.

‘‘Our job is to not allow the other team to score,’’ Mack said. ‘‘I feel like we’ve been talking about [the Bears’ offense struggling], and I don’t really care about that part. Our part is to shut offenses out, and we’re capable of doing that.’’

With this much talent, it’s not crazy for the Bears’ defensive standard to be perfection. Near-perfect doesn’t cut it for Mack. He doesn’t want an off-night by quarterback Mitch Trubisky or anyone else to derail him.

It’s clearer than ever that not only did the Bears acquire a rare talent, but they acquired him at exactly the right point in his career.

They might need some shutouts to win — that’s what it would’ve taken last week — and Mack is willing to demand that out of his half of the locker room. He remains, just as he was last season, the top reason this team can call itself a contender.

5 key statistics to monitor ahead of SMU vs. Texas State, including the Mustangs’ impressive interception streak – The Dallas Morning News

5 key statistics to monitor ahead of SMU vs. Texas State, including the Mustangs' impressive interception streak - The Dallas Morning News nevin manimala
5 key statistics to monitor ahead of SMU vs. Texas State, including the Mustangs' impressive interception streak - The Dallas Morning News nevin manimala

UNIVERSITY PARK — SMU (2-0) hosts Texas State (0-2) with the hopes of remaining perfect through three games for the first time in 35 years. While the Mustangs have many key pieces that are new to the team this year, a lot of the offensive output is coming from players who are returning.

Quarterback Shane Buechele is new to the offense, but he looks like he’s been there a while. That’s thanks, in part, to players like Reggie Roberson Jr., James Proche and Xavier Jones, whose scoring has been known on the Hilltop before this season.

In that sense, it will be a proven commodity on Saturday, against a much less proven commodity in TSU. The Sun Belt foes have had a strong defensive start, but have struggled to score.

Let’s look beyond those numbers a bit at five key stats heading into this Week 3 game.

13 straight games

When safety Patrick Nelson picked off Mason Fine on Saturday, it marked the 13th straight game that the Mustangs have recorded at least one interception. That streak ranks as the fifth longest in the entire country. And that’s especially impressive when you consider that the Mustangs defense last season ranked, as a whole, among the worst in the FBS.

Saturday might present a good opportunity for SMU to make it 14 straight games. Texas State has worked with two quarterbacks, and they’ve posted two touchdowns through the air, while getting picked off six times in two weeks.

5 pass breakups

This plays off the first point, at least a little bit. But SMU head coach Sonny Dykes announced on Monday that Brandon Stephens was put on scholarship. It was clearly a quick decision, after the UCLA graduate transfer played so well in the first two games this year. He’s tied for most in the nation with five pass breakups.

His story continues to be impressive. After receiving offers out of Plano High School to be a collegiate defensive back, he went the route of a running back. Three years later, he’s changed his mind, and it clearly is working for him.

mobile-only dfpPosition1

8 penalties

SMU has won the penalty battle so far this season. With just eight penalties for 82 total yards, the impact has been somewhat negligible for the Mustangs. There have been some useless ones, like the Trevor Denbow targeting and the Cam’ron Jones blindside tackle.

But for the most part, it’s been the opposition that’s been committing penalties. SMU has drawn 173 yards worth of penalties. Which, if it were just a single team, would be good for 119th out of 130 Division I teams. SMU, for perspective, ranks No. 41 in penalty yards per game.

Texas State, despite being poor on offense, actually has only 59 total yards of penalties this season, and has drawn 76 yards.

3 red zone trips

Texas State has played one really good team in Texas A&M and one solid team in Wyoming. Still, the Bobcats have been in the red zone just three times all season thus far. More than that, they’ve scored just once in those three chances.

SMU made it to the red zone twice that many times in the first three quarters of its game vs. UNT. Offense has been a big problem for TSU, and they have just three TDs all year and have yet to score a point in the third quarter of a game. Much like SMU, TSU’s kicking game is also unreliable, converting 0-for-2 field goals so far.

58 rushing yards

Texas State wouldn’t run the ball that much, even if it had a really strong offense. But even with a bad offense, 58 rushing yards is pretty weak. The Bobcats have 131 yards in gains, and 73 negative yards this season. No player has ran the ball more than 14 times this season, but it’s still an average of 1.6 yards per carry.

A lot of this has to do with quarterbacks being taken down. But even taking that out, there just won’t be much of a threat for SMU, like there was with North Texas’ Tre Siggers last week.

Arkansas 2020 commitment statistics – NWAOnline

Arkansas 2020 commitment statistics - NWAOnline nevin manimala
Arkansas 2020 commitment statistics - NWAOnline nevin manimala

Arkansas 2020 commitment statistics

• Season totals listed under individual player:

POS NAME HT WT. 40 SCHOOL TONIGHT

OL TY’KIEAST CRAWFORD 6-7 323 NA Carthage, Texas vs. Marshall

• 2 pancake blocks, no sacks allowed

ATH KELVONTAY DIXON 6-0 173 4.4 Carthage, Texas vs. Marshall

• 6-132 receiving, 4 TDs, 3-26 rushing

DE TYRECE EDWARDS 6-3 240 NA Knoxville (Tenn.) West vs. Jefferson Co.

• 29.5 TT, 6.5 TFL, 3.5 SA

LB DREW FRANCIS 6-2 200 4.79 Knoxville (Tenn.) West vs. Jefferson Co.

• 19 TT, 5 TFL, 1 BP for a TD

TE BRANDON FRAZIER 6-7 231 NA McKinney (Texas) North vs. Wakeland

• 3-57 receiving, 1 TD

LB MARTAVIUS FRENCH 6-2 236 4.7 Memphis Whitehaven at Brentwood Acad.

• 16 TT, 6 TFL, 1 FF, 1 INT

ATH JOHN GENTRY 5-10 190 4.56 Houston North Shore vs. Westfield

• 5-117 receiving, 2 TDs, 10-61 rushing

TE ALLEN HORACE 6-5 242 NA Crockett, Texas at Grapeland

• 4-117 receiving

WR MASON MANGUM 5-11 171 4.45 Austin (Texas) Westlake vs. Akins

• 13-166 receiving, 1 TD

QB CHANDLER MORRIS 5-10 172 4.5 Dallas Highland Park vs. Lone Star

• 48-68-687 passing, 6 TDs, 4 INTs, 26-229 rushing, 7 TDs

DE JASHAUD STEWART 6-2 224 4.61 Jonesboro at Conway

• 13 UT, 3 AT, 4 TFL, 4 SA, 7 QB hurries, 1 RF

DE BLAYNE TOLL 6-6 244 4.88 Hazen at Bearden

• 4 TT, 3 TFL, 2-45 receiving

ATH DARIN TURNER 6-3 215 NA Memphis East at Bartlett

• 3-60 receiving, 1 TD, 5 TT, 1 INT

CB JAMIE VANCE 5-11 170 NA N.O. Eda Karr at McDonogh (Thur.)

• N/A

OLB CATRELL WALLACE 6-6 212 NA Bryant vs. Bentonville West

• 6 TT, 1 TFL

WR SAVION WILLIAMS 6-5 195 NA Marshall, Texas at Carthage

• 8-99 receiving, 2 TDs

LAST WEEK

TY’KIEAST CRAWFORD (No sacks allowed in a 49-7 victory over Liberty-Eylau); KELVONTAY DIXON (3-100 receiving, 2 TDs, 1-11 rushing in a 49-7 victory over Liberty-Eylau); TYRECE EDWARDS ( 5.5 TT, .5 TFL, .5 SA in a 45-0 victory over Karns); DREW FRANCIS ( 8 TT, 2 TFL, 1 BP, 1 TD in a 45-0 victory over Karns); BRANDON FRAZIER (No stats in a 68-65 victory over Northwest); MARTAVIUS FRENCH (10 TT, 3 TFLS, 1 FF, 1 INT in a 23-0 victory over North Little Rock); JOHN GENTRY (2-36 receiving, 1 TD, 9-55 rushing in a 38-7 victory over Ridge Point); ALLEN HORACE (3-52 receiving in a 38-32 loss to Garrison); MASON MANGUM (5-63 receiving in 35-7 victory over Cypress Ranch); CHANDLER MORRIS (19-26-208 passing, 1 TD, 2 INTs, 8-44 rushing, 3 TDs in 52-25 victory over Mesquite Horn); JASHAUD STEWART (3 UT, 2 AT, 2 QB hurries in a 35-7 victory over Batesville); BLAYNE TOLL (Open date); DARIN TURNER (2-47 receiving, 1 TD, 2 TT, 1 INT in 35-28 victory over Collierville); JAMIE VANCE (NA in 42-39 loss to John Curtis Christian); CATRELL WALLACE (Open date); SAVION WILLIAMS (No stats in 53-0 loss to Longview)

Sports on 09/13/2019

Nebraska high school football statistics leaders, Sept. 12 | Football – Omaha World-Herald

Nebraska high school football statistics leaders, Sept. 12 | Football - Omaha World-Herald nevin manimala

Nebraska high school football statistics leaders, as published in The World-Herald.

RUSHING Att. Yds. Avg. TD

Halleen, Lincoln SE 52 342 171.0 4

Harris, Millard South 27 277 138.5 5

Braasch, Columbus 26 268 134.0 2

Valencia, Millard West 36 262 131.0 0

Maessner, Kearney 38 257 128.5 3

Hustad, Elkhorn South 29 236 118.0 6

Wright, North Platte 39 225 112.5 0

Ducker, Bellevue West 17 222 111.0 6

Price, Papillion-LV 39 204 104.5 0

Macumber, Elkhorn 28 198 99.0 x

PASSING C-A-I Yds. Avg. TD

Glantz, Bellevue West 30-44-0 608 304.0 7

Burke, Omaha Burke 26-43-0 358 179.0 3

Cooper, Fremont 24-38-0 350 175.0 5

Cahoy, Grand Island 25-38-0 340 170.0 4

Crandall, Papio South 17-24-1 314 157.0 2

Payton, Westside 14-20-0 314 157.0 4

Coniglio, Cre. Prep 27-36-0 310 155.0 3

Barrientos, Om. South 15-32-3 302 151.0 7

Murray, Kearney 12-31-3 277 138.5 4

Urban, Millard South 20-35-0 273 136.5 3

RECEIVING No. Yds. Avg. TD

Watts, Omaha Burke 17 221 110.5 2

Dengel, Bellevue East 9 220 110.0 0

Stroh, Kearney 7 213 106.5 4

Griggs, Omaha South 7 203 101.5 5

Taylor, Millard South 6 187 93.5 3

Betts, Bellevue West 7 186 93.0 3

I. Appleget, Lincoln SE 8 176 88.0 1

Douglass, Grand Island 8 174 87.0 4

Helms, Bellevue West 6 160 80.0 1

Weidemann, Om. Westside 2 77 77.0 0

SCORING TD FG EP Pts. Avg.

Griggs, Omaha South 6 0 0 36 18.0

Ducker, Bellevue West 6 0 0 36 18.0

Hustad, Elkhorn South 6 0 0 36 18.0

Gomes, Millard West 5 0 0 30 15.0

Larson, Lincoln East 5 0 0 30 15.0

Douglass, Grand Island 5 0 0 30 15.0

PUNTING No. Yds. Avg.

Larson, Lincoln East 4 45.0 60

Franke, Gretna 6 40.0 47

Mikkelsen, Bellevue East 2 40.0 40

Foster, Bellevue East 6 39.7 43

Mangelsen, Norfolk 10 39.4 71

Hohl, Lincoln SW 6 38.5 40

Diaz, Omaha Central 5 37.8 46

Lampkin, Omaha Burke 11 37.5 68

Scholl, Gretna 6 37.2 40

Johnson, Lincoln East 4 36.3 40

TACKLES UT AT TT Avg.

Unassisted tackles 2 points, assists 1 point

Larchick, Gretna 14 8 36 18.0

Loergan, Lincoln Pius X 15 4 34 17.0

Kelly, Millard North 11 11 33 16.5

Rogers, Papio South 9 14 32 16.0

McCurdy, Fremont 13 5 31 15.5

Splater, Norfolk 9 13 31 15.5

Terrell, Omaha Bryan 13 4 30 15.0

Interceptions: 3, Stone, Fremont. 2, Hinken, Mckinnis, GI; Lojing, Fremont; Griggs, Forget, McGary, Om. South; Closman, MN

TEAM OFFENSE Rush Pass Avg.

Bellevue West 411 610 510.5

Millard South 609 307 459.0

Columbus 583 263 423.0

Omaha South 519 302 410.5

Grand Island 425 368 396.5

Elkhorn South 606 149 377.5

Kearney 473 277 375.0

Omaha Westside 421 314 367.5

Lincoln Southeast 475 255 365.0

Omaha North 491 227 359.0

RUSHING Att. Yds. Avg. TD

Krul, Scottsbluff 35 371 185.5 4

Harsh, Scottsbluff 24 321 160.5 6

Gordon, Omaha Skutt 24 235 117.5 3

Diesing, Omaha Skutt 34 232 116.0 3

Nieman, Waverly 33 224 112.0 3

Madden, Ralston 14 223 111.5 4

Ramos, Lexington 24 200 100.0 2

Paces, Ralston 30 199 99.5 1

Canoyer, Waverly 33 183 91.5 5

PASSING C-A-I Yds. Avg. TD

Bohn, Bennington 83-133-4 784 392.0 3

Synek, Hastings 34-54-1 532 266.0 3

Dotzler, Om. Roncalli 30-48-1 471 235.6 5

Oerter, Norris 22-37-0 423 211.5 3

DeMayo, Elkhorn MM 24-39-1 400 200.0 3

Stewart, Blair 36-61-3 387 193.5 1

Carpenter, Lexington 15-32-2 324 162.0 0

Gordon, Omaha Skutt 18-31-x 323 161.5 4

Dubray, Alliance 22-48-x 280 140.0 2

Myers, Seward 20-38-3 275 137.5 4

RECEIVING No. Yds. Avg. TD

Roepke, Elkhorn MM 13 311 155.5 3

Corrigan, Bennington 18 201 100.5 5

Richman, Lexington 7 181 90.5 1

S. Orr, Omaha Roncalli 8 160 80.0 3

Nauert, Hastings 5 157 78.5 1

Juengst, Grand Island NW 6 152 76.0 3

Shoemaker, Hastings 10 152 76.0 1

Schmaderer, Bennington 8 149 74.5 2

Puck, Bennington 13 140 70.0 0

Fenoglio, Omaha Roncalli 9 140 70.0 1

SCORING TD FG EP Pts. Avg.

Canoyer, Waverly 5 0 7 37 18.5

Harsh, Scottsbluff 6 0 0 36 18.0

Gordon, Omaha Skutt 3 0 13 31 15.5

Corrigan, Bennington 5 0 0 30 15.0

PUNTING No. Yds. Avg.

Sanders, Grand Island NW 3 46.3 55

Brinker, Omaha Skutt 4 39.8 —

Eggert, Plattsmouth 8 38.8 48

Molgaard, Ralston 3 37.7 40

Williams, Norris 6 35.5 —

Kalvelage, Hastings 8 35.1 43

TACKLES UT AT TT Avg.

Kavulak, Seward 12 17 41 20.5

Meneses, Plattsmouth 14 10 38 19.0

Folchert, Alliance 13 8 34 17.0

Heaton, Grand Island NW 8 16 32 16.0

Meyer, Norris 6 19 31 15.5

Erwin, York 8 14 30 15.0

Interceptions: 2, Pohlmeier, Plattsmouth; Richman, Lex; Schawang, Wav.; Schmoker, McCook

TEAM OFFENSE Rush Pass Avg.

Scottsbluff 813 187 500.0

Omaha Skutt 628 323 475.5

Bennington 78 793 435.5

Omaha Roncalli 260 498 379.0

Hastings 203 532 367.5

Norris 295 423 359.0

Waverly 630 63 346.5

Ralston 522 169 345.5

Lexington 260 395 327.5

OTHER OMAHA-AREA SCHOOLS

RUSHING Att. Yds. Avg. TD

Egr, Yutan 51 397 198.5 5

Christensen, Platteview 52 352 176.0 3

Torosian, Concordia 25 258 129.0 3

Knott, Louisville 36 220 110.0 3

Luben, Wahoo 25 205 102.5 5

Lilly, Wahoo Neumann 35 203 101.5 6

Grafelman, Brownell Talbot 21 201 100.5 3

PASSING C-A-I Yds. Avg. TD

Torosian, Concordia 24-42-2 447 223.5 2

Halford, Fort Calhoun 23-46-2 394 197.0 4

C. Pugsley, Br. Talbot 18-27-0 341 170.5 3

Washburn, Ashland 23-48-1 253 126.5 3

Miller, Arlington 17-25-0 240 120.0 3

Waido, Wahoo 17-25-0 227 113.5 4

RECEIVING No. Yds. Avg. TD

Pittman, Arlington 10 183 81.5 0

T. Pugsley, Br. Talbot 7 148 74.0 1

Haag, Mead 4 123 61.5 1

Strauss, Fort Calhoun 8 119 59.5 2

Walling, Wahoo 6 118 59.0 3

Jacobsen, Ashland-GW 5 111 55.5 2

Domsch, Concordia 3 110 55.0 2

SCORING TD FG EP Pts. Avg.

C. Pugsley, Br Talbot 7 0 8 50 25.0

Lilly, Wahoo Neumann 8 0 0 48 24.0

Bergstrom, Om. Christian 5 0 8 38 19.0

Egr, Yutan 5 0 2 32 16.0

Luben, Wahoo 5 0 0 30 15.0

TACKLES UT AT TT Avg.

Therkildsen, Fort Calhoun 17 8 42 21.0

Bordovsky, Wahoo 14 10 38 19.0

Millikan, Platteview 13 10 36 18.0

Grafelman, Brownell Talbot 7 19 33 16.5

Snipes, Conestoga 11 11 33 16.5

Kolterman, Wahoo 10 12 32 16.0

Dierks, Fort Calhoun 12 6 30 15.0

Interceptions: 3, Haag, Mead. 2, T. Pugsley, OBT; Jacobsen, Ash-GW

TEAM OFFENSE Rush Pass Avg.

Omaha Brownell-Talbot 566 391 478.5

Omaha Concordia 438 447 442.5

Yutan 671 115 393.0

Ashland-Greenwood 457 268 362.5

Wahoo 443 227 335.0

No report — Class A: Omaha Benson. Class B: Gering, Omaha Gross, South Sioux City. Omaha-area: Elmwood-Murdock, Weeping Water.