Traditional IT support metrics often reward superficial volume and speed, missing the deeper business value and real-world impact of support teams. Inspired by advanced sports analytics, this project introduces a data-driven, multi-dimensional framework for measuring, optimizing, and communicating the true value of support teams—including time management, scalability, work complexity, user experience, and direct revenue protection.
- Introduction
- Why Advanced Metrics?
- Available Metrics
- Business Impact
- Implementation
- Data Requirements
- Sample Usage
- Quick Start
- Contributing
- License
Just as advanced statistics revolutionized baseball by revealing the hidden value of players, Sabre-Metrics was built to move support metrics beyond "tickets resolved" and "average time" into metrics that capture real impact. Our framework equips IT and support leaders with the tools to:
- Benchmark and reward meaningful contributions
- Minimize wasteful support activities
- Tie IT directly to business value
This project is the result of extensive field experience, observation, and analysis, with formulas refined specifically for real-world team environments.
Traditional metrics such as "tickets resolved" or "average resolution time" fail to:
- Account for the varying complexity of tickets (password reset ≠ outage resolution)
- Recognize the risk to business revenue from critical failures
- Prevent agents from avoiding or gaming the system (avoiding tough cases)
- Measure scalability—how well agents maintain efficiency as their workload grows
- Quantify the cost of support to end-user productivity and satisfaction
Our metrics, informed by the lessons of SABRmetrics in sports, shift the focus to a holistic, value-based approach for all stakeholders.
Measures: Weighted time management across multiple ticket lifecycle stages to reflect overall efficiency.
Measures: Weighted completion of tickets by business impact, motivating agents to address higher-value work.
Measures: An all-in-one metric that synthesizes time efficiency and complexity work, similar to WAR (Wins Above Replacement) in baseball.
Measures: How well an agent maintains efficiency as their assigned workload increases—true "scalability."
Measures: The cumulative time burden and number of touchpoints imposed on users per ticket, surfacing user experience bottlenecks.
Measures: The actual business revenue placed at risk by delayed or failed support, directly connecting support performance to financial outcomes.
Full formulas and methodology for each metric are included in the repository.
By adopting these metrics:
- Leaders can see which support agents deliver the highest value—not just the highest output.
- Teams gain insights into where process improvements can most benefit users.
- Executives receive clear, dollar-quantified evidence of IT’s true business contribution.
Organizations implementing this approach have demonstrated:
- Improved support staff engagement and retention
- Higher end-user satisfaction and productivity
- Reduced revenue-at-risk through proactive management
See our thesis paper in this repo for a deep dive and case study analysis.
- Data on ticket/resolution times, agent actions, ticket categorization (complexity/priority), endpoints and office locations, and revenue
- Ability to aggregate and process data in SQL, Python, Excel, PowerBI, or similar platforms
- Data Collection: Aggregate data from ITSM platforms (e.g., ServiceNow, JIRA, Zendesk)
- Metric Calculation: Apply provided formulas (SQL/Python templates included)
- Benchmarking: Establish baselines and ranges within your organization
- Review & Iterate: Visualize results, gather feedback, adjust weights/thresholds
- Report: Present findings in dashboards and to leadership
| Metric | Required Fields |
|---|---|
| ATS | Resolution time, response time, first response time per ticket |
| TCS | Ticket type/priority and count |
| RTV | Output of ATS * TCS |
| SPS | # Tickets per agent, average res time, team averages |
| UFS | Agent reply count, reassign counts, ticket open time, ticket count |
| TRI | Ticket impact level, duration, endpoint/office data, revenue |
Example—Agent Time Score (SQL):
SELECT
agent_id,
100 / (
(AVG(resolution_time_hrs) * 0.5) +
(AVG(response_time_hrs) * 0.2) +
(AVG(first_response_time_hrs) * 0.3)
) AS agent_time_score
FROM tickets
GROUP BY agent_id;See the docs folder for more detailed steps for each metric.
You can also experiment with the formulas using the Python helper script:
python examples/calculate_metrics.py ats 2 1 0.5See docs/quick_start.md for a brief tutorial on running these metrics in Python.
We welcome contributions, discussion, and extension of these metrics! Please see CONTRIBUTING.md for guidelines.
Areas for collaboration include:
- Industry-specific weighting schemas
- Visualization/dashboard integration
- Additional business-alignment metrics
This project is licensed under the GNU General Public License v3.0—see the LICENSE file for details.
Please cite this repository, its authors/maintainers, or our THESIS.md in presentations or derivative works.
Questions, feedback, or success stories?
Open an issue, pull request, or start a discussion!