Data generated by a 13.1-mile footrace held in Pittsburgh, Pennsylvania, typically includes finishing times for each participant, often categorized by age group and gender. This data may also encompass overall placement, pace information, and potentially qualifying times for other races. An example would be a publicly accessible online database listing every runner’s bib number, name, and corresponding finish time.
Access to this information provides runners with a performance benchmark, allowing them to track progress, identify areas for improvement, and compare their results against others. It also serves a historical record of the event, documenting individual achievements and the overall competitive landscape. For race organizers, this data is invaluable for logistical planning, participant engagement, and potentially attracting sponsors. Furthermore, the published outcomes contribute to the broader running community, fostering a sense of accomplishment and inspiring future participation.
Further exploration may include analyzing trends in finishing times, examining the demographics of participants, and discussing the impact of weather conditions on race performance. Information regarding the course layout, registration procedures, and past race results can also provide valuable context.
1. Official Times
Official times represent the core component of race results, providing a precise measurement of participant performance. Accuracy and reliability are paramount, as these times determine placements and potential qualifications. Understanding the nuances of official timing systems and their impact on recorded results is crucial for interpreting the data.
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Gun Time vs. Chip Time
Gun time refers to the time elapsed from the starting signal to when a runner crosses the finish line. Chip time, measured by an electronic device, records the precise duration from when a runner crosses the starting line to crossing the finish line. In large races like a half marathon, chip time provides a more accurate representation of individual performance, especially for those starting further back in the pack. For instance, a runner’s gun time might be 1:45:00, but their chip time, reflecting their actual running duration, could be 1:40:00.
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Timing Technology
Modern timing systems utilize various technologies, such as RFID chips, to ensure accurate and efficient recording of times. These systems detect runners’ progress at designated checkpoints, providing split times and verifying completion of the course. The precision of these technologies eliminates potential discrepancies and contributes to the integrity of the results. For instance, disposable bib tags containing RFID chips allow for seamless tracking without requiring runners to return equipment.
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Data Processing and Validation
After a race, the collected data undergoes processing and validation to ensure accuracy. This involves filtering out anomalies, correcting potential errors, and producing the final official times. This meticulous process maintains the credibility of the results and ensures fair competition. For example, instances of a chip malfunctioning would be addressed during this phase.
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Public Availability and Presentation
Official times are typically published online, often within hours of the race’s conclusion. This allows participants to readily access their performance data and compare it against others. The presentation of this data may include various sorting and filtering options, such as age group, gender, or overall finish order, facilitating analysis and comparison.
The meticulous recording, processing, and presentation of official times contribute significantly to the value and impact of race results. These times serve as a quantifiable measure of achievement, motivating runners and providing a benchmark for future performance. The transparency and accessibility of this information reinforce the integrity of the competition and enhance the overall race experience. By analyzing trends and patterns within official times, one can gain insights into the competitive landscape and identify potential areas for individual improvement within the context of the Pittsburgh Half Marathon.
2. Age Group Rankings
Age group rankings provide a nuanced perspective on individual performance within the Pittsburgh Half Marathon results. By categorizing runners based on age, these rankings offer a more relevant comparison than overall results, acknowledging the physiological differences across age groups and promoting fair competition. Analyzing age group performance can reveal trends and patterns specific to certain demographics within the race.
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Competitive Landscape within Age Groups
Age group rankings allow runners to gauge their performance relative to their peers. This fosters a more targeted competitive environment, motivating individuals to strive for improvement within their specific age bracket. For example, a 40-year-old runner can directly compare their performance against other runners in the 40-44 age group, providing a more meaningful assessment than comparing against all runners, including those in their 20s or 60s. This focused competition can encourage continued participation and personal growth.
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Identifying Age-Related Performance Trends
Examining results across different age groups can illuminate trends and patterns related to age and running performance. This information can be valuable for individual runners, coaches, and researchers studying the effects of aging on athletic performance. For instance, analyzing average finishing times within each age group might reveal the age range where peak performance is typically observed in half marathon runners, providing valuable insights for training and performance optimization.
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Recognizing Outstanding Achievement within Age Groups
Awarding top finishers within each age group highlights achievements that might be overlooked in the overall rankings. This recognition celebrates dedication and athleticism across all ages and encourages broader participation. For example, a runner who finishes 50th overall might be the top finisher in their age group, earning a deserved recognition that reflects their strong performance relative to their peers. This contributes to a more inclusive and motivating race environment.
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Impact on Race Strategy and Training
Understanding age-graded performance can inform training strategies and race-day tactics. Runners can use this information to set realistic goals and adjust their pacing based on typical performance within their age group. For instance, a runner aiming to be competitive within their age group can analyze previous years’ results to determine the typical pace and finishing times of top performers in their category, helping them develop a targeted training plan and a more effective race strategy.
By incorporating age group rankings, the Pittsburgh Half Marathon results offer a more comprehensive and insightful view of individual and collective performance. This nuanced approach fosters a more inclusive and motivating environment for all participants, promoting healthy competition and recognizing achievements across diverse demographics. The data derived from these rankings provides valuable information for both individual runners seeking to improve and researchers studying the interplay between age and athletic performance in the context of long-distance running.
3. Gender Placements
Gender placements within the Pittsburgh Half Marathon results provide a crucial lens for analyzing performance and participation trends. By categorizing results based on gender, the data reveals insights into the relative competitiveness of male and female participants, allows for targeted comparisons, and promotes inclusivity within the sport. This categorization acknowledges physiological differences between genders, creating a fairer platform for evaluating performance and celebrating achievements. For example, separate awards and recognition for top female finishers highlight accomplishments that may not be apparent in the overall rankings, encouraging broader female participation and recognizing exceptional athleticism within the female running community.
Analyzing gender-specific data within the Pittsburgh Half Marathon results allows for tracking progress and identifying disparities in participation rates or performance trends over time. This information can be invaluable for race organizers, researchers, and advocacy groups working to promote equity and inclusivity in running. For instance, observing a consistent gap in average finishing times between genders could prompt further investigation into potential factors, such as training access or societal influences, ultimately contributing to a more informed understanding of gender dynamics within the sport. Furthermore, tracking the number of female participants year over year provides insights into the effectiveness of outreach programs aimed at increasing female engagement in long-distance running.
Understanding the significance of gender placements in the context of the Pittsburgh Half Marathon results contributes to a more comprehensive and equitable analysis of the event. This detailed perspective not only acknowledges and celebrates athletic achievements within distinct gender categories but also provides valuable data for studying participation trends and promoting inclusivity. By examining gender-specific data, the running community gains valuable insights into the dynamics of competition and can work towards creating a more equitable and welcoming environment for all participants.
4. Overall Finish Order
Overall finish order represents a fundamental component of Pittsburgh Half Marathon results, providing a comprehensive ranking of all participants regardless of age or gender. This ranking, based solely on the time taken to complete the course, establishes a clear hierarchy of performance, identifying the fastest runners in the field. Examining the overall finish order offers insights into the competitive landscape of the race, highlighting exceptional performances and establishing a benchmark for future events. For instance, comparing the top finishers’ times across multiple years can reveal trends in elite-level performance and the evolving competitiveness of the race. Furthermore, understanding the distribution of finishing times across the entire field can inform race organizers about participant demographics and potential adjustments to race logistics or qualifying standards.
The overall finish order serves several crucial functions within the context of race results. It provides a straightforward metric for recognizing and celebrating top performers, fostering a sense of accomplishment and motivating participants. This ranking also plays a crucial role in determining qualification for other races, using established time standards within the overall finish order. For example, a runner aiming to qualify for a prestigious marathon might need to finish within a certain percentile of the overall finish order in a qualifying half marathon. Furthermore, the overall finish order data allows for statistical analysis, enabling comparisons between different races and identifying trends in participant performance over time. This information can be valuable for researchers studying athletic performance and for race organizers seeking to improve the event and cater to participant needs.
In summary, the overall finish order constitutes a critical element of Pittsburgh Half Marathon results, offering a clear performance hierarchy and serving multiple practical purposes. From recognizing top athletes to informing future race strategies, the overall finish order provides valuable insights for participants, organizers, and researchers alike. While age and gender rankings offer important contextual perspectives, the overall finish order remains the definitive measure of performance within the race, shaping the narrative of the event and influencing future participation and training endeavors. Challenges in ensuring the accuracy and integrity of the overall finish order, particularly in large races, necessitate robust timing systems and data validation processes. Understanding the limitations and potential biases associated with relying solely on overall finish order emphasizes the importance of considering other data points like age group and gender rankings for a more comprehensive analysis of race performance.
5. Pace Analysis
Pace analysis plays a crucial role in understanding Pittsburgh Half Marathon results, providing insights beyond finish times. A runner’s pace, typically measured in minutes per mile, reveals how consistently they maintained speed throughout the 13.1-mile course. Examining pace data alongside overall results allows for a more nuanced interpretation of performance. For example, a runner with a faster finish time might have employed a variable pace strategy, starting fast and slowing down later, while a runner with a slightly slower finish time might have maintained a more consistent pace throughout. Analyzing split times, recorded at designated points along the course, further refines this understanding, revealing how pace changes over different segments of the race, influenced by factors like terrain, weather, or individual race strategy. This granular view of performance facilitates identification of strengths and weaknesses, informing future training and race strategies.
The practical significance of pace analysis extends beyond individual runners. Coaches can utilize pace data to develop personalized training programs, targeting specific areas for improvement. Race organizers can analyze aggregate pace data to understand participant behavior and optimize course design or aid station placement. For example, if a significant portion of runners consistently slow down at a particular point in the course, it might indicate a challenging section requiring additional support or course modification. Furthermore, understanding typical pacing strategies within specific demographics, like age or gender groups, can contribute to more informed race predictions and personalized training advice. Comparing pace data across multiple races allows runners to track their progress and assess the effectiveness of training regimens. For instance, a runner consistently improving their average pace over successive half marathons demonstrates tangible progress, regardless of variations in course difficulty or weather conditions.
In conclusion, pace analysis provides a critical layer of understanding within Pittsburgh Half Marathon results. Moving beyond simply acknowledging finish times, pace analysis delves into the nuances of individual performance and reveals valuable insights for runners, coaches, and race organizers. Challenges in accurately capturing and interpreting pace data, particularly in mass participation events, emphasize the importance of reliable timing technologies and data validation procedures. Integrating pace analysis with other performance metrics like heart rate or power output further enhances the depth of understanding, moving towards a more holistic and data-driven approach to analyzing and improving running performance. The insights gained from pace analysis contribute not only to individual achievement but also to the broader understanding of human performance within the context of endurance running.
6. Qualifying Standards
Qualifying standards often represent a critical connection between Pittsburgh Half Marathon results and participation in higher-level races. Many prestigious marathons and other competitive running events require runners to achieve a qualifying time in a certified half marathon, such as the Pittsburgh Half Marathon. Achieving a qualifying time, typically verified through official race results, grants access to registration for these sought-after events. This creates a direct link between performance in Pittsburgh and opportunities for further competition. For example, the Boston Marathon utilizes a qualifying time system based on age and gender, meaning a strong performance in the Pittsburgh Half Marathon could secure a coveted spot in the Boston Marathon. The presence of qualifying standards adds another layer of significance to Pittsburgh Half Marathon results, transforming the race into not only a standalone event but also a potential gateway to higher levels of competition.
The impact of qualifying standards extends beyond individual runners. Race organizers in Pittsburgh benefit from attracting a more competitive field, enhancing the prestige and visibility of the event. The association with qualifying standards elevates the perceived importance of the race, potentially attracting sponsors and media attention. Furthermore, local businesses and the tourism industry can experience positive economic impact from increased participation by runners seeking qualifying times. Understanding the specific qualifying times required for various target races influences training strategies and race-day goals for runners participating in the Pittsburgh Half Marathon. For instance, a runner aiming to qualify for the Chicago Marathon might adjust their pacing strategy in Pittsburgh to maximize their chances of achieving the required qualifying time. This targeted approach highlights the practical significance of understanding qualifying standards and their connection to race results.
In summary, the relationship between qualifying standards and Pittsburgh Half Marathon results creates a dynamic interplay between individual performance, race prestige, and access to higher-level competition. Challenges in meeting increasingly stringent qualifying standards highlight the evolving competitive landscape of long-distance running. Furthermore, variations in qualifying times across different races necessitate careful planning and strategic race selection for runners pursuing specific competitive goals. Understanding the nuances of qualifying standards adds a crucial dimension to the analysis of Pittsburgh Half Marathon results, connecting individual achievement to broader opportunities within the running community. The influence of qualifying standards underscores the importance of accurate timing, transparent results reporting, and accessible information regarding qualifying criteria for various target races. This understanding empowers runners to make informed decisions about their training, race selection, and pursuit of competitive aspirations.
7. Participant Data
Participant data forms the foundation of Pittsburgh Half Marathon results, encompassing a range of information beyond mere finishing times. This data typically includes runner identification (name, bib number), demographics (age, gender, location), and performance metrics (split times, overall pace). The collection and processing of participant data are essential for generating comprehensive race results. For instance, accurate bib number association ensures correct matching of individual runners with their respective times, while demographic information allows for segmented analysis of results by age group or gender. The depth and accuracy of participant data directly impact the quality and usefulness of the published race results. Incomplete or erroneous data can lead to inaccuracies in rankings, skewed statistical analysis, and diminished trust in the integrity of the results. Conversely, comprehensive and validated participant data enables detailed performance analysis, identification of trends within specific demographics, and ultimately, a more enriching experience for both participants and race organizers. The cause-and-effect relationship between accurate participant data and reliable race results is fundamental to the event’s value.
Beyond its role in generating results, participant data serves several other important functions. Aggregated data provides valuable insights into participant demographics, informing race organizers about the geographic reach of the event, the popularity of specific age groups, and the overall growth of participation over time. This information can be leveraged for marketing strategies, targeted outreach programs, and resource allocation decisions. Furthermore, participant data can be utilized to personalize the race experience. Pre-race communication, such as personalized emails with race-day instructions or post-race feedback surveys, can enhance participant engagement and foster a stronger sense of community. Analyzing participant data also contributes to improved race logistics. Understanding peak participation times at various points along the course can inform aid station placement, crowd control measures, and overall course design, leading to a smoother and safer race experience for all.
In summary, participant data represents a crucial element of Pittsburgh Half Marathon results, extending beyond its functional role in generating accurate rankings. Its impact on race organization, marketing strategies, and participant engagement underscores the importance of data integrity and responsible data management practices. Challenges in collecting and processing large volumes of participant data, while ensuring privacy and accuracy, necessitate robust data management systems and adherence to ethical data handling guidelines. The strategic utilization of participant data contributes not only to the success of the individual race but also to the broader development and sustainability of the running community.
8. Historical Comparisons
Historical comparisons provide valuable context for interpreting current Pittsburgh Half Marathon results. By analyzing data from previous races, performance trends, participation patterns, and the evolution of the event itself become evident. This historical perspective adds depth to the understanding of present-day outcomes, revealing not only individual progress but also broader shifts within the running community and the race’s overall trajectory.
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Performance Trend Analysis
Comparing finishing times and pace data across multiple years reveals performance trends within specific demographics, such as age groups or gender categories. Analyzing these trends can illuminate the impact of training methodologies, evolving race strategies, or even broader societal factors influencing participation in long-distance running. For example, a consistent decrease in average finishing times over several years might suggest improved training resources or increased popularity of specific running programs. Conversely, a plateau or increase in times could indicate challenges related to course modifications or declining participation within certain demographics. This historical analysis provides crucial context for interpreting current performance levels and predicting future trends.
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Participation Rate Evolution
Tracking participation rates over time provides insights into the growth and evolution of the Pittsburgh Half Marathon. Increasing registration numbers could indicate growing interest in long-distance running within the region, the success of race marketing efforts, or the event’s increasing prestige within the running community. Declining participation might signal competition from other races, changing demographics within the region, or the need for adjustments to race logistics or marketing strategies. Analyzing historical participation data provides a valuable metric for assessing the long-term health and sustainability of the event.
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Course and Condition Comparisons
Considering variations in course layout, weather conditions, and other logistical factors across different years provides crucial context for comparing race results. For instance, a particularly hot or humid year might explain slower average finishing times compared to a cooler year. Similarly, changes in the course route, such as the inclusion of more hills or challenging terrain, could also influence overall performance. Accounting for these historical variations ensures a fairer comparison of results across different years and a more accurate assessment of actual performance improvements or declines.
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Competitive Landscape Shifts
Analyzing the field of competitors across multiple years can reveal shifts in the competitive landscape of the Pittsburgh Half Marathon. An influx of elite runners might lead to faster overall times and increased competition for top placements. Conversely, a decrease in elite participation could create opportunities for other runners to achieve higher rankings. Understanding these historical shifts provides valuable context for interpreting current race results and anticipating future competitive dynamics. Examining the historical presence of sponsored athletes or running clubs can also illuminate the evolving role of external influences within the race.
By integrating historical comparisons, analysis of Pittsburgh Half Marathon results gains depth and nuance. This longitudinal perspective reveals not only individual progress but also broader trends shaping the race and the running community it represents. Understanding these historical patterns provides invaluable context for interpreting current performance, predicting future outcomes, and ensuring the continued growth and success of the Pittsburgh Half Marathon.
9. Course Information
Course information plays a crucial role in interpreting Pittsburgh Half Marathon results. A thorough understanding of the course layout, elevation changes, and other relevant details provides valuable context for analyzing performance outcomes. Factors such as terrain, course difficulty, and even the prevailing weather conditions on race day can significantly influence runners’ pace, strategy, and ultimately, their finishing times. Examining course information alongside race results allows for a more nuanced understanding of individual and overall performance trends.
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Elevation Profile
The elevation profile, a visual representation of the course’s elevation changes, provides crucial insights into the race’s difficulty. A course with significant elevation gain and loss presents different challenges compared to a flat course. Runners often adjust their pacing strategies based on elevation changes, conserving energy on uphill sections and capitalizing on downhill segments. The elevation profile informs analysis of split times, providing context for fluctuations in pace throughout the race. For example, slower split times on uphill sections and faster times on downhill sections are expected. Understanding the elevation profile allows for a more informed interpretation of performance variations and a fairer comparison of results across different races with varying elevation profiles.
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Course Terrain
The type of terrainpaved roads, gravel paths, trail sectionscan significantly influence running performance. Different surfaces impact foot strike, energy expenditure, and traction, influencing pacing and overall race strategy. For example, a course with significant off-road sections might yield slower times compared to a fully paved course. Analyzing race results in conjunction with course terrain information helps explain performance variations and contextualize individual and overall outcomes. Knowledge of the course terrain also informs shoe selection and training preparation, optimizing performance for specific race conditions.
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Turns and Curves
The frequency and severity of turns and curves along the course can affect a runner’s pace and efficiency. Sharp turns require adjustments in stride and momentum, potentially impacting overall speed. A course with numerous tight turns might lead to slightly slower times compared to a straighter course. Understanding the course layout, including the number and nature of turns, provides valuable context for analyzing pace variations and overall race performance. Examining split times in relation to course turns can reveal segments where runners experience greater challenges in maintaining pace. This information can be utilized for developing more effective race strategies, focusing on maintaining momentum through turns and minimizing energy expenditure.
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Weather Conditions
Weather conditions on race day, including temperature, humidity, wind speed, and precipitation, can significantly impact race performance. High temperatures and humidity can increase physiological stress, leading to slower times and increased risk of heat-related issues. Strong headwinds can impede progress, while tailwinds can offer an advantage. Analyzing race results in conjunction with weather data provides crucial context for interpreting performance outcomes. For example, slower overall times in a race with high temperatures and humidity are expected. Understanding the influence of weather conditions allows for a fairer comparison of results across different races and informs preparation strategies for future events, including appropriate clothing choices and hydration plans.
Integrating course information with an analysis of Pittsburgh Half Marathon results provides a richer, more nuanced understanding of performance. Considering the interplay of these factors offers a comprehensive perspective, allowing runners, coaches, and race organizers to glean more meaningful insights from the data. By understanding the impact of course characteristics on race outcomes, runners can make more informed decisions about training strategies, race-day pacing, and overall performance goals, contributing to a more strategic and fulfilling running experience.
Frequently Asked Questions about Pittsburgh Half Marathon Results
This section addresses common inquiries regarding race results, aiming to provide clarity and facilitate informed interpretation of the data.
Question 1: Where can official results for the Pittsburgh Half Marathon be found?
Official results are typically published online on the designated race website shortly after the event’s conclusion. They may also be accessible through partnered timing companies’ platforms.
Question 2: What is the difference between gun time and chip time?
Gun time represents the time elapsed from the starting signal to when a runner crosses the finish line. Chip time, measured by an electronic device, records the precise duration from when a runner crosses the starting line to crossing the finish line. Chip time offers a more accurate individual performance metric, especially in larger races.
Question 3: How are age group rankings determined?
Age group rankings categorize runners based on pre-assigned age brackets, allowing for comparison against others within the same age range. This provides a more relevant performance assessment than overall rankings, acknowledging physiological differences across age groups.
Question 4: What if there is a discrepancy in recorded results?
Runners should contact the race organizers or the timing company directly to address any discrepancies. Providing supporting evidence, such as photos or witness accounts, can aid the review process.
Question 5: How can historical results be accessed?
Historical race results are often archived on the race website or timing company platforms. These archives can provide valuable context for analyzing current performance trends and understanding the evolution of the event.
Question 6: How do course conditions affect race results?
Course characteristics, such as elevation changes, terrain type, and weather conditions, significantly influence race performance. Examining these factors alongside results data provides a more comprehensive understanding of individual and overall outcomes.
Understanding these key aspects of race results facilitates a more informed interpretation of performance data and contributes to a more enriching race experience. Accurate and accessible results data empowers runners to track progress, identify areas for improvement, and fully appreciate their achievements within the context of the Pittsburgh Half Marathon.
Further information may be found on the official race website or by contacting the race organizers directly.
Tips for Utilizing Pittsburgh Half Marathon Results
Examining race data strategically can provide valuable insights for runners seeking improvement and a deeper understanding of performance.
Tip 1: Analyze Pace Variations. Don’t solely focus on finish times. Review split times to understand pace variations throughout the course. Identify consistent pacing or sections where significant slowdowns occurred, informing future training and race strategies. For example, consistent positive splits (slower second half) may indicate a need for improved endurance training.
Tip 2: Compare Against Personal Bests. Track performance across multiple races, comparing current results against personal bests. This provides a quantifiable measure of progress, independent of external factors like course difficulty or weather. Consistent improvement in personal bests demonstrates effective training and progress toward long-term goals.
Tip 3: Utilize Age Group Rankings. Evaluate performance within specific age groups for a more relevant comparison, acknowledging physiological variations across age categories. This provides a more focused benchmark for improvement than comparing solely against overall rankings.
Tip 4: Consider Course Conditions. Factor in course difficulty, elevation changes, and weather conditions when interpreting results. Slower times on a challenging course with significant elevation gain in hot weather are expected. Contextualizing results based on course and weather data offers a more accurate performance assessment.
Tip 5: Study Historical Data. Review past race results, considering personal performance trends over time. This longitudinal perspective provides insights into training effectiveness and progress, independent of variations in individual race conditions.
Tip 6: Leverage Data for Goal Setting. Utilize race results to establish realistic and achievable goals for future races. Analyzing past performance and identifying areas for improvement informs targeted training plans and goal-oriented race strategies.
Tip 7: Seek Professional Guidance. Consult with certified running coaches or experienced trainers to interpret results and develop personalized training plans. Professional expertise provides individualized insights, tailored to specific performance goals and training needs.
Strategic analysis of race results empowers runners to achieve informed self-assessment, identify areas for growth, and develop targeted training plans, fostering ongoing improvement and a deeper understanding of personal performance within the context of the Pittsburgh Half Marathon.
By applying these tips, runners can glean actionable insights from race results, contributing to a more fulfilling and successful running journey. The transition to a concluding section will summarize the key takeaways and offer final recommendations.
Pittsburgh Half Marathon Results
Exploration of Pittsburgh Half Marathon results reveals a multifaceted data set offering valuable insights into individual performance, race trends, and the broader running community. From official times and age group rankings to pace analysis and historical comparisons, each data point contributes to a deeper understanding of this prominent athletic event. Course information, including elevation profiles and terrain details, adds crucial context for interpreting outcomes. Qualifying standards link performance in Pittsburgh to broader competitive opportunities, while participant data provides a foundation for personalized experiences and informed race management. Understanding the nuances of data interpretation empowers runners and organizers alike to extract maximum value from the information.
The data generated by the Pittsburgh Half Marathon represents more than just a record of finishing times; it embodies the culmination of individual dedication, strategic training, and the collective pursuit of athletic achievement. Continued analysis of this data promises not only individual growth but also the advancement of the sport and the enduring vitality of the Pittsburgh Half Marathon within the broader running landscape. Further exploration of performance metrics, coupled with advancements in data analysis techniques, will undoubtedly unlock even deeper insights into human performance within the context of endurance running.
